tag:blogger.com,1999:blog-31514769476736174412024-03-22T04:32:21.618+02:00Secure SystemsLearning to build systems that are secure, easy-to-use, and deployable.Lachlan J Gunnhttp://www.blogger.com/profile/14291915024226566435noreply@blogger.comBlogger15125tag:blogger.com,1999:blog-3151476947673617441.post-43335428421197093562023-06-15T12:37:00.000+03:002023-06-15T12:37:22.974+03:00ML Security at SSG: Past, Present and Future<p style="text-align: left;"><span style="color: #666666;"> <span style="font-family: Arial; font-size: 11pt; font-style: italic; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">“Security is a process not a product”</span><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;"> – Bruce Schneier</span></span></p><span id="docs-internal-guid-ae432943-7fff-9ec0-7950-684a84606d6a"><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">This year, we wrap up an eight-year-long, machine learning (ML) focused research effort at <a href="https://ssg.aalto.fi" target="_blank">Secure Systems Group</a> at Aalto University. In that period, we went from using ML </span><span style="font-family: Arial; font-size: 11pt; font-style: italic; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">for security</span><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">, to exploring whether ML-based systems are v</span><span style="font-family: Arial; font-size: 11pt; font-style: italic; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">ulnerable to novel attacks</span><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;"> and how to defend against them.</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">We started off by using ML to provide security guarantees to various systems. We used ML models to e.g., <a href="https://ieeexplore.ieee.org/abstract/document/7926371" target="_blank">detect phishing websites</a>, <a href="https://ieeexplore.ieee.org/abstract/document/8884802" target="_blank">detect adversaries in IoT systems</a>, or <a href="https://dl.acm.org/doi/abs/10.1145/3359789.3359810" target="_blank">discover financial fraud in a payment platform</a>. However, in the process, we realised that in many domains, ML models are not just an isolated component or a tool but the </span><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; font-weight: 700; vertical-align: baseline; white-space: pre-wrap;">core</span><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;"> of the system. This in turn, led us to look into the security of the models </span><span style="font-family: Arial; font-size: 11pt; font-style: italic; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">themselves</span><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">.</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">We quickly learnt that models are quite brittle – they can be fooled using evasion attacks (both in <a href="https://dl.acm.org/doi/abs/10.1145/3338501.3357366" target="_blank">vision</a>, and <a href="https://dl.acm.org/doi/abs/10.1145/3270101.3270103" target="_blank">text</a> domains), <a href="https://ieeexplore.ieee.org/abstract/document/8806737" target="_blank">stolen by a malicious client</a>, and are difficult to use in a <a href="https://dl.acm.org/doi/abs/10.1145/3133956.3134056" target="_blank">privacy-preserving manner</a>. To address these issues, we spent years looking into ways of protecting them, focusing on model extraction and ownership. We and others have shown that model extraction attacks are a <a href="https://link.springer.com/chapter/10.1007/978-3-030-62144-5_4" target="_blank">realistic threat</a>. We proposed <a href="https://dl.acm.org/doi/abs/10.1145/3474085.3475591" target="_blank">the first model watermarking scheme designed to deter model extraction</a>. Fingerprinting schemes have emerged as one of the most promising defences against model extraction. We have <a href="https://arxiv.org/abs/2210.13631" target="_blank">highlighted concerns</a> with leading fingerprinting schemes. In particular, we have highlighted that robustness against malicious accusers is an understudied aspect in the literature – <a href="https://arxiv.org/abs/2304.06607" target="_blank">we have shown that all existing watermarking and fingerprinting schemes are vulnerable to malicious accusers</a>.</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">Having many people with industry background, in our research, we’ve continuously focused on how our ideas can be integrated into systems, and given attention to real-world deployment considerations. In particular, we raised a concern that is typically overlooked in academic literature: practitioners have to deploy defences <a href="https://arxiv.org/abs/2207.01991" target="_blank">against multiple security concerns simultaneously; sometimes these interact negatively</a>. Understanding the interaction between different defences remains an important open problem.</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">All in all, our research output has been a collective effort of many researchers across two universities, and thanks to many collaborations – academic and industrial. <a href="https://crysp.uwaterloo.ca/research/SSG/" target="_blank">Secure Systems Group continues its work at the University of Waterloo</a>, continuing on a broad range of ML security and privacy topics including the exploration of how defences against a particular concern influence other concerns, and </span><a href="https://arxiv.org/abs/2204.09649" style="text-decoration: none;"><span style="color: #1155cc; font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; text-decoration-skip-ink: none; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap;">what hardware security mechanisms can be used to secure ML models</span></a><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">.</span></p><br /><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><a href="https://ssg.aalto.fi/wp-content/uploads/2023/06/2023DemoDay-MLSec@AaltoUW-forwebsite.pdf" style="text-decoration: none;"><span style="color: #1155cc; font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; text-decoration-skip-ink: none; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap;">Poster PDF</span></a><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;"> presented at the annual </span><a href="https://ssg.aalto.fi/events/secure-systems-demo-day-2023/" style="text-decoration: none;"><span style="color: #1155cc; font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; text-decoration-skip-ink: none; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap;">SSG Demo Day 2023</span></a><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">.</span></p><a href="https://ssg.aalto.fi/research/projects/mlsec" style="text-decoration: none;"><span style="color: #1155cc; font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; text-decoration-skip-ink: none; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap;">SSG ML research page</span></a><span style="font-family: Arial; font-size: 11pt; font-variant-alternates: normal; font-variant-east-asian: normal; font-variant-ligatures: normal; font-variant-numeric: normal; font-variant-position: normal; vertical-align: baseline; white-space: pre-wrap;">.</span></span>Sebastian Szyllerhttp://www.blogger.com/profile/14715178898589546785noreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-45611190719148998662023-06-05T18:23:00.002+03:002023-06-05T18:24:06.367+03:00Better visualisation of consensus protocols<p><em>Almost every BFT paper includes an "arrow diagram", showing who sends how many messages to whom and when. Despite their universality, these diagrams are difficult to interpret, leaving out many important details and including unimportant ones. In this blog post, we describe a new way to visualise BFT protocols that is easier to draw, easier to read, and easier to interpret.</em></p>
<h2>Arrow diagrams in BFT</h2>
<p>Earlier I presented a new Byzantine fault tolerance (BFT) protocol, <a href="https://arxiv.org/abs/1905.10255">SACZyzzyva</a>, at <a href="https://doi.org/10.1109/SRDS47363.2019.00024">SRDS 2019</a>. Afterwards, I received a number of positive comments about this talk, and in particular about the fact that it did not include a single "arrow diagram". </p>
<p>If you don't know what I mean by an arrow diagram (or even if you do), then <a href="https://www.usenix.org/system/files/login/articles/13_mickens.pdf">this article</a> by James Mickens in <em>;login:</em> provides an excellent introduction to the BFT literature, having this to say on arrow diagrams:
<blockquote>
<em>In a paper about Byzantine fault tolerance, the related work section will frequently say, "Compare the protocol diagram of our system to that of the best prior work. Our protocol is clearly better." The paper will present two graphs that look like Figure 2. Trying to determine which one of these hateful diagrams is better is like gazing at two unfathomable seaweed bundles that washed up on the beach and trying to determine which one is marginally less alienating.
</em></blockquote> </p>
<p>Here is one that we included in our paper on SACZyzzyva:</p>
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj-pI3yarOvTBqPleDraPDaVsbKyknNABlrRFuHF5uZ91XfRMxmNDkblPfZt_vHrWg35MiWwRrn8ai1Ekh6zzQ02-Np6BdK6V4eeQiXXBnpyKIhA785INy7PrS1eWfPUlxpyxH4pfK9NSA/s1600/saczyzzyva-arrows.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj-pI3yarOvTBqPleDraPDaVsbKyknNABlrRFuHF5uZ91XfRMxmNDkblPfZt_vHrWg35MiWwRrn8ai1Ekh6zzQ02-Np6BdK6V4eeQiXXBnpyKIhA785INy7PrS1eWfPUlxpyxH4pfK9NSA/s400/saczyzzyva-arrows.png" width="400" height="191" data-original-width="640" data-original-height="305" /></a></div>
<p>Each arrow represents a message from one machine to another, and the difference in colour (grey vs black) shows how SACZyzzyva transactions can complete with two fewer rounds of communication than Zyzzyva transactions. This isn't the clearest diagram—it is difficult for the brain to pick out which lines it should be counting, so a quick skim of this figure doesn't do much except show whether each phase has <em>O</em>(1), <em>O</em>(<em>n</em>), or <em>O</em>(<em>n</em><sup>2</sup>) messages with respect to the number of replicas.</p>
<p>But this diagram also hides some important features of the two protocols that can make a big difference to performance. First, Zyzzyva doesn't always need the extra rounds of communication—when all replicas respond, then it is just as fast as SACZyzzyva. If these extra rounds are needed rarely enough, then using SACZyzzyva might not be worthwhile. Secondly, it doesn't say anything about the <em>size</em> of the messages; the size of each of Zyzzyva's <span style="font-variant: small-caps">prepare</span> messages actually increases <em>O</em>(<em>n</em>) with the number of replicas, giving it a bit-complexity of <em>O</em>(<em>n</em><sup>2</sup>) whenever there are actually faults to tolerate. This suggests that SACZyzzyva may have better scalability than Zyzzyva in normal operation, but you can't see this in the arrow diagram.</p>
<h2>Ball diagrams</h2>
<p>If not arrow diagrams, then what? The key observation is that when comparing these diagrams, the reader doesn't pay much attention to the sender or recipient of the message, but only the <em>number</em> of messages. This lets us simplify the diagram dramatically:</p>
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxMQaWhMEhY6sT1Y22QCqsQmfSgL8BhFKUz39hnInTBwuFRzJV8Epv9tG0S_fKHmeB4Tzt6SfG_upOdG4rVuWAwfBEWeGz3pPBnwmdSK7XZUrHfkuz44bikM5wXi8ZBV8xIoGNZvUqS9A/s1600/zyzzyva-balls.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjxMQaWhMEhY6sT1Y22QCqsQmfSgL8BhFKUz39hnInTBwuFRzJV8Epv9tG0S_fKHmeB4Tzt6SfG_upOdG4rVuWAwfBEWeGz3pPBnwmdSK7XZUrHfkuz44bikM5wXi8ZBV8xIoGNZvUqS9A/s400/zyzzyva-balls.png" width="400" height="213" data-original-width="1492" data-original-height="795" /></a></div>
<p>In this diagram, it is much easier to see how many messages are sent in each round. We also show that the second phase is triggered by a timeout—meaning that there is a long wait between the two bracketed parts—but that it shouldn't happen very often—indicating that the first phase might be more important from a performance perspective.</p>
<p>Ball diagrams are also much less busy, making it easier to fit other information into the diagram:</p>
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQd2OnTP2-uJGfocy8BgXjasx5E-bVZV3Ia4_JniI74L6Irc_CpMgnOhbk40EDWUllC47atfPD4oEasQx4ZH_K78bpwymUdV1N9sCgjUh06D5caF8rf8HgJwdcVqv9cQ9vidKNLn7awVw/s1600/saczyzzyva-normal-balls.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhQd2OnTP2-uJGfocy8BgXjasx5E-bVZV3Ia4_JniI74L6Irc_CpMgnOhbk40EDWUllC47atfPD4oEasQx4ZH_K78bpwymUdV1N9sCgjUh06D5caF8rf8HgJwdcVqv9cQ9vidKNLn7awVw/s400/saczyzzyva-normal-balls.png" width="400" height="293" data-original-width="768" data-original-height="563" /></a><br/><caption><strong>Figure 3</strong>: The SACZyzzyva normal-case protocol, annotated with the changes relative to Zyzzyva's.</caption></div>
<h2>Showing message size</h2>
<p>However, these diagrams don't show the relative sizes of the messages: the diagram shows the <em>message</em> complexity, but not the <em>bit</em> complexity. We can visualise this by making the area of each ball proportional to the size of its message. In the case of Zyzzyva, the <span style="font-variant: small-caps">request</span> and <span style="font-variant: small-caps">order-request</span> messages may be quite large relative to the <span style="font-variant: small-caps">spec-response</span> messages, since they need to contain request data, and the size of the <span style="font-variant: small-caps">commit</span> messages will depend on the number of replicas, since they contains 2<em>f</em>+1 signatures from other replicas:</p>
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg0NYwlyseoNB5-1ggccKK7bAcQVEXQcyjBCFDatkBOZATYkMhTaCPsbJ-t-mnveR59FxYmUCsGg7KE7g7DTL6UCK2SjH0ue-Cx6nfnV_BHvecZuhZ8oHUBgrzmEKOaMauTZO90sPpfAv4/s1600/zyzzyva-balls-size-4.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg0NYwlyseoNB5-1ggccKK7bAcQVEXQcyjBCFDatkBOZATYkMhTaCPsbJ-t-mnveR59FxYmUCsGg7KE7g7DTL6UCK2SjH0ue-Cx6nfnV_BHvecZuhZ8oHUBgrzmEKOaMauTZO90sPpfAv4/s640/zyzzyva-balls-size-4.png" height="158" data-original-width="1188" data-original-height="509" /></a></div>
As the number of replicas increases, both the number of messages and their relative sizes change, showing that as the number of replicas increases, scalability will ultimately be limited by the number and size of the <span style="font-variant: small-caps">order-request</span> messages may be quite large relative to the <span style="font-variant: small-caps">spec-response</span> messages, since they need to contain relevant data, and the size of the <span style="font-variant: small-caps">commit</span> messages:
<div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgtgfWPwBK1bQtKW9wqzcC_wsLptBMUDxsU_58uEYLvQrNDYV_u5IMW7TrtGfdZ_qLTl_5wbP1gxQRrkaSVOk1ku323agtetJDg_P3-xLOFA7oZcqOKrhQ2nOnYF_gub2Y5TA2LEJEcCS4/s1600/zyzzyva-balls-size-25.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgtgfWPwBK1bQtKW9wqzcC_wsLptBMUDxsU_58uEYLvQrNDYV_u5IMW7TrtGfdZ_qLTl_5wbP1gxQRrkaSVOk1ku323agtetJDg_P3-xLOFA7oZcqOKrhQ2nOnYF_gub2Y5TA2LEJEcCS4/s640/zyzzyva-balls-size-25.png" width="512" data-original-width="1600" data-original-height="494" /></a></div>
<p>This fact is invisible on a regular arrow diagram.</p>
<h2>Conclusion</h2>
<p>This post introduced ball diagrams, new type of diagram for visualisation of BFT protocols. These diagrams are easier to read than the usual arrow diagrams that appear in most BFT papers and presentations, so if you are less interested in the source/destination information than the raw number of bits or messages, then consider giving these a try in your next presentation.</p>Lachlan J Gunnhttp://www.blogger.com/profile/14291915024226566435noreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-71347055731929892162019-06-20T12:31:00.002+03:002019-06-26T14:58:19.101+03:00Historical insight into the development of Mobile TEEs<div dir="ltr" style="text-align: left;" trbidi="on">
<div class="MsoNormal">
<i>Today, <a href="https://doi.org/10.1109/MSP.2014.38">Trusted Execution Environments (TEEs) </a>are ubiquitous
in mobile devices from all major smartphone vendors. They first appeared in
Nokia smartphones fifteen years ago. Around the turn of the century, Nokia
engineers in Finland played a crucial role in developing mobile TEE technology
and paving the way for its widespread deployment. But this important story is not
widely known as there is limited public documentation. To address this gap of
“missing history”, we carried out an oral history project during the spring of 2019.
In this post, we summarize our findings.</i></div>
<div class="MsoNormal">
<br /></div>
<h2 class="MsoNormal" style="text-align: left;">
Historical insight into TEEs</h2>
<div class="MsoNormal" style="text-align: justify;">
Trust in mobile devices is a prerequisite for the modern
mobile industry ranging from e-commerce to social media. Customers, companies,
and regulators need to trust the mobile phone with sensitive information such
as credit card numbers and fingerprints. A <a href="https://doi.org/10.1109/MSP.2014.38">trusted execution environment (TEE)</a>
— a secure area that runs a computing system in parallel with, but isolated
from, the main operating system — is central to modern mobile security. Through
creating technical conditions for different stakeholders to rely on the mobile
system, it has been an essential component in the development of the modern
mobile business in general. </div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
During the spring of 2019, the <a href="https://ssg.aalto.fi/">Secure Systems Group at Aalto University</a> hosted an oral history project on the development of<span style="mso-spacerun: yes;"> </span>mobile TEEs. The project focused on the role
played by Nokia experts in the emergence and establishment of mobile TEEs. We
conducted a series of interviews with fifteen key actors: senior directors and
managers, researchers, and security professionals. The aim was to increase the
understanding of the mobile platform security systems today and to recognize
the human actors behind their development and widespread deployment.</div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
The starting point in any historical inquiry is that
technological development is never self-evident nor pre-determined, neither
does it takes place in isolation. Instead, technological systems are developed
by individuals at a certain place and time, restricted by different economic,
technical, regulatory, and other factors. Understanding the development of the
mobile secure execution system helps us to master it today and to improve it
tomorrow. </div>
<h2 class="MsoNormal" style="text-align: left;">
Emergence of mobile security</h2>
<div class="MsoNormal" style="text-align: justify;">
Communication is imperative for modern societies. The
history of telecommunication goes back centuries from visual signalling, e.g.,
the semaphore system to the electronic telegraph and radio transmission. A
common theme in the history of telecommunication is that security follows the
communication technology with a delay. In Finland, the first public mobile
network, the <a href="https://en.wikipedia.org/wiki/Autoradiopuhelin">Autoradiopuhelin</a> (ARP) and the first generation <a href="https://en.wikipedia.org/wiki/Nordic_Mobile_Telephone">Nordisk MobilTelefon</a> (NMT) system transmitted analog voice signal originally without any
cryptographic protection. Yet, the security of the device itself was hardly
recognized as a critical problem before the 1990s. The primary reason for that
was the strictly regulated operation: both ARP (launched in 1971) and NMT
(1981) were operated by governmental organisations. Mobile phones themselves
were closed systems with few extra capabilities compared with traditional
landline phones: The bulky and heavy equipment were best protected by doors and
locks.<span style="mso-spacerun: yes;"> </span></div>
<div class="MsoNormal" style="text-align: justify;">
<span style="mso-spacerun: yes;"> </span></div>
<div class="MsoNormal" style="text-align: justify;">
The 1990s revolutionized mobile telecommunication. First,
the GSM standard addressed the communication security problem by encrypting the
signal and protecting the air-interface. Second, the deregulation of
telecommunication opened market for private companies which skyrocketed the
number of operators within a short time. The physical dimensions of mobile
handsets shrank, making them attractive targets for thieves. Finally, after the
mid-1990s, support for third-party applications written in the Java programming
language that could be downloaded from the Internet transformed phones rapidly
from closed devices to open systems that increasingly started to resemble
small, general-purpose computers. Resulting from more users, less governmental
control over the industry, and more sources of potential vulnerabilities,
device security emerged as a new problem in the design of mobile phones. </div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
The security of the phone under these new conditions
referred merely to the integrity of the device: Regulators and mobile network operators
came up with novel needs to protect certain pieces of information inside the
phone from unauthorized changes after the phone left the assembly line. In
particular, the regulators wanted a secure storage for the device identity
(International Mobile Equipment Identity, IMEI) and for certain parameters such
as those for radio frequency transmission, which could affect the safety of the
phone and functionality of the mobile network. Mobile operators, which were the
principal customers for Nokia, needed a strong subsidy lock mechanism
(colloquially known as “SIM locks”) that would tie the phone to a certain
operator for a predetermined time. </div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
Security of Nokia’s Digital Core Technology (DCT) generation
phones was enforced with mainly software solutions and protected by secrecy
within the organization: even security professionals had little more than
educated guesses about the structure and requirements of the DCT security
architecture. The essential weakness of this kind of “security through
obscurity” design is that after the secret designs are revealed, the protection
is lost. The high market share of Nokia made it an attractive target for
hackers. DCT4 generation brought in hardware component in the form of
one-time-programmable memory but particularly in the case of the SIM locks, the
economic motives to break the security system outstripped the technological
capabilities to protect it. The profit losses of important customers increased
pressures to design a better security architecture.<span style="mso-spacerun: yes;"> </span></div>
<h2 class="MsoNormal" style="text-align: justify;">
Towards mobile platform security </h2>
<div class="MsoNormal" style="text-align: justify;">
The interest towards a coherent, hardware-enforced platform
security stemmed from a team of engineers working with mobile payments and
security. The initial idea was to introduce a separate security chip to
implement physical isolation of security-critical processing. Yet, an
additional hardware chip was deemed too expensive in the strictly
cost-sensitive organization. At the turn of the millennium, a newly graduated
engineer came up with an idea to implement a logically isolated security mode
using just one chip. A new status bit was introduced in the processor, which
determined the status of data stored in memory and whether or not the processor
was in a secure mode. This “secure processor environment” design was adopted as
the fundamental cornerstone of the next baseband generation: <a href="https://en.wikipedia.org/wiki/Base_Band_5">Baseband 5 </a>(BB5). </div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
In the history of technology no technological innovations
really emerge out of nowhere but<span style="mso-spacerun: yes;"> </span>are
always influenced by preceding ideas and inventions. Certain features of the
secure environment, were decisively invented around already existing
patents.<span style="mso-spacerun: yes;"> </span>What was novel with Nokia’s
solution was the combination of the software and hardware features to implement
an architecture for mobile platform security which were deployed on a
large-scale.</div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
The launch of the <a href="https://en.wikipedia.org/wiki/Nokia_6630">first BB5 phone in 2004</a> was a major
landmark in the development of Nokia’s mobile phones as it effectively ushered
in the era of 3G phones. Less visible to the public but equally important was
the changes introduced to the platform security model as a whole. <span style="mso-spacerun: yes;"> </span></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
First, it marked a switch from “security through obscurity”
towards “security through transparency”. Open communication throughout the
process, considerable level of transparency of the design, together with a
public key infrastructure was required to develop a strong, usable, and
cost-efficient security architecture. </div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
Second, security transformed from add-on feature into an
integral part of the platform design. At the time when the early smartphones
started to increasingly resemble general purpose computers in terms of their
capabilities and function, the security engineers opted against following the
PC world in the security design: instead of reactive firewalls and anti-virus
tools, mobile security was better addressed proactively in the platform design.
</div>
<h2 class="MsoNormal" style="text-align: left;">
Alliance of hardware and software</h2>
<div class="MsoNormal" style="text-align: justify;">
At the time when the secure processor environment was
initiated, Nokia produced its own Radio Application Processor (RAP) chipsets.
It simplified the efforts of making the hardware components to accommodate
security requirements. Yet, the first BB5 phones were already equipped with
<a href="http://docplayer.net/5825930-Omap-platform-security-features.html">Texas Instruments OMAP processors</a>. Nokia and Texas Instruments had a close
partnership which involved intense cooperation in chipset design. Later, Texas
Instruments branded the technology as <a href="http://www.ti.com/pdfs/wtbu/ti_mshield_whitepaper.pdf">M-Shield</a>. M-Shield stemmed from the same
origin as<span style="mso-spacerun: yes;"> </span>Nokia’s secure processor
environment but was subsequently developed in a different direction. </div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
Around 2003, ARM proposed to develop a system-wide hardware
isolation for secure execution for Nokia. Cooperation between ARM and Texas
Instruments had its independent business goals separate from Nokia’s needs but
it was also in Nokia’s interests. It provided Nokia with a possibility to
implement a secure environment on any chip implementing ARM’s security architecture,
which would later become known as <a href="https://developer.arm.com/ip-products/security-ip/trustzone">ARM TrustZone</a>. (A 2004 article is possibly the first public technical paper describing ARM TrustZone. At present, there is no official website hosting this article, but it appears to be <a href="http://docplayer.net/51242536-Trustzone-integrated-hardware-and-software-security-enabling-trusted-computing-in-embedded-systems.html">stashed at this unofficial site</a>). </div>
<h2 class="MsoNormal" style="text-align: left;">
Security as an enabler</h2>
<div class="MsoNormal" style="text-align: justify;">
A deep paradox in the development of security technology is
that security is important to have but difficult to sell. The importance of
security becomes apparent only when it does not work and its benefits for the
business are rarely manifested by increased sales. In corporate management,
security remained overshadowed by competition for customers’ satisfaction and
optimization of global supply chains. Instead of a strategic R&D project,
the secure processor environment proceeded as a technology-driven skunkworks of
a handful of engineers<span style="mso-spacerun: yes;"> </span>and researchers.
The development of security technology outside the strategic spotlights was
facilitated by Nokia’s organizational culture that granted technological
experts with considerable room to maneuver. Also critical was that the security
engineers successfully translated security from a problem into an enabler.
E-commerce still remained a marginal use case at the beginning of the 2000s, SIM
lock, IMEI protection, and later digital rights management (DRM) became the
main business cases that justified the adjustment of the hardware and software
architectures. </div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
Once the platform security architecture was accepted for
product programs, hardware suppliers had adopted the secure processor
environment in their designs, and complementary adjustments in manufacturing
process and key distribution services were implemented, the security technology
constituted an infrastructure for other applications and functions to take
advantage of. These novel uses of the security infrastructure ranged from the
rather trivial case of the protection of audio compression attributes of Nokia
headphones to the widely influential use of security certificates for distinguishing among model variants
during the manufacturing process. </div>
<h2 class="MsoNormal" style="text-align: left;">
Standardised trust</h2>
<div class="MsoNormal" style="text-align: justify;">
After the adoption of the hardware-enforced secure execution
environment as a de facto internal standard, Nokia turned the attention onto
the state-of-the art of mobile security standards in formal standards
development organizations. Two prime rationales motivated the representatives
of the company to take an active role in international standardization forums.
First, an open standard that was revised in an international cooperation
community, required no maintenance from Nokia, and was available for potential
suppliers would facilitate competitive bidding in chipset production. Second,
as the mobile standards were anyway going to take form in the future, Nokia
wanted to make sure they would be compatible with the solution it had already
adopted. </div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
At first, Nokia’s representatives chaired a mobile working
group within <a href="https://trustedcomputinggroup.org/">Trusted Computing Group</a> (TCG). Although being founded by PC
companies, TCG was the only industrial forum working with hardware security
standards for global use in the early 2000s. In 2007, TCG announced the first
hardware security standard for mobile, Mobile Trusted Platform Module (mobile
TPM, MTM), which became an <a href="https://www.iso.org/standard/50970.html">ISO standard</a>. It was different from Nokia’s secure
processor environment but more importantly, it was compatible with it.</div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
The concept of TEEs was first described publicly by <a href="https://en.wikipedia.org/wiki/Open_Mobile_Terminal_Platform">Open Mobile TerminalPlatform (OMTP)</a> in its specification for <a href="http://www.omtp.org/OMTP_Advanced_Trusted_Environment_OMTP_TR1_v1_1.pdf">Advanced Trusted Environment</a> in 2009.
A while later, the center of TEE standardization became another industry
forum, <a href="https://globalplatform.org/specs-library/?filter-committee=tee">GlobalPlatform</a>. With two industrial forums striving to standardize
hardware-enforced mobile security, there was a risk that they would end up with
mutually incompatible specifications. It was in Nokia’s interest to turn the
forums from competitors into cooperators. In 2010, GlobalPlatform published its
first TEE standard, <a href="https://globalplatform.org/specs-library/tee-client-api-specification/">TEE client API 1.0</a> that defines the communication between
trusted applications which are executed in TEE, and applications executed by
the main operating system. In 2012, GlobalPlatform and TCG announced the
founding of a joint working group focusing on security topics.</div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
After 2010, Nokia had less resources for extensive mobile device R&D
projects or participation in international forums. Development of TEE technology continued even as Nokia's role in it diminished over time. Today, TEE
technology is widely deployed on mobile devices and is extensively used on both
iOS and Android smartphones.</div>
<h2 class="MsoNormal" style="text-align: left;">
Some concluding remarks</h2>
<div class="MsoNormal" style="text-align: justify;">
History does not repeat itself. Lessons of past failures and
success are not readily applicable in the future. Yet, historical insight into
the development of mobile TEEs helps us to comprehend<span style="mso-spacerun: yes;"> </span>comparable systems of today. In particular,
despite the convergence between the PC and mobile worlds, the different approaches
to platform security architecture still manifest the legacy of the past: The technological paths once taken create dependencies over time and continue shaping the framework in which the security engineers operate today. <br />
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
In addition, the constitutive role of only a few dedicated
security professionals in just one company in the development and establishment
of an international standard demonstrates the malleability of technological
systems when they are still under construction.<span style="mso-spacerun: yes;">
</span></div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<div class="MsoNormal" style="text-align: justify;">
Finally, the concept of “security” resonates with the
complex needs of authorities, customers, and users but translates into very
different meanings for different stakeholders. Mobile security technology may
protect the privacy of the user from corporations, or the business from its
customers; it may also ensure the safety of the device or enable the
law-enforcement to<span style="mso-spacerun: yes;"> </span>access personal data
in a lawful manner. Security technology is bound to the<span style="mso-spacerun: yes;"> </span>interaction with its political, cultural, and
economic environment and is always shaped by them.</div>
<div class="MsoNormal" style="text-align: justify;">
<br /></div>
<h4 class="MsoNormal">
The TEE-history project team: </h4>
<div class="MsoNormal">
Saara Matala, project researcher</div>
<div class="MsoNormal">
<a href="https://thomasnyman.fi/">Thomas Nyman</a>, doctoral candidate </div>
<div class="MsoNormal">
<a href="https://asokan.org/asokan/">N. Asokan</a>, professor</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
<i>
</i><br />
<div class="MsoNormal">
<i>We interviewed fifteen former or current Nokia employees for
this project: two senior executives, three managers, four researchers, and<span style="mso-spacerun: yes;"> </span>six engineers. We thank them for being
generous with their time and insights. Among them are:</i><br />
<ul>
<li><i>Timo Ali-Vehmas, Nokia</i></li>
<li><i>Jan-Erik Ekberg, Huawei</i></li>
<li><i>Janne Hirvimies, Darkmatter</i></li>
<li><i>Antti Jauhiainen, ZoomIN Oy</i></li>
<li><i>Antti Kiiveri, Boogie Software Oy</i></li>
<li><i>Markku Kylänpää, VTT</i></li>
<li><i>Meri Löfman, Brighthouse Intelligence Oy </i></li>
<li><i>Janne Mäntylä, Huawei</i></li>
<li><i><i>Yrjö Neuvo, Aalto University</i> </i></li>
<li><i>Valtteri Niemi, University of Helsinki</i></li>
<li><i>Lauri Paatero, F-Secure</i></li>
<li><i>Jukka Parkkinen, OP Financial Group</i></li>
<li><i>Janne Takala, Profit Software Oy.</i></li>
<li><i>Janne Uusilehto, Google </i></li>
</ul>
</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<br /></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-42725000996236658352019-06-05T10:19:00.000+03:002019-06-05T10:19:21.486+03:00Protecting against run-time attacks with Pointer Authentication<i>Since the Morris Worm of 1988, buffer overflows and similar have been the source of many remote-code-execution vulnerabilities. They can allow attackers to overwrite pointers in memory and make a vulnerable program jump to unexpected locations. The ARMv8.3-A architecture includes </i><a href="https://community.arm.com/developer/ip-products/processors/b/processors-ip-blog/posts/armv8-a-architecture-2016-additions">Pointer Authentication</a><i>, a set of instructions that can be used to cryptographically authenticate pointers and data before they are used. We show several ways that Pointer Authentication can be used to improve security, and prevent attackers from turning programmer errors into remote-code-execution vulnerabilities.</i><br />
<div>
<h2>
Pointer Authentication: the What, the How and the Why</h2>
The fundamental purpose of Pointer Authentication (PA) is to allow software to verify that values read from memory—whether data or pointers—were generated by the same process in the right context. It does this by allowing software to generate a <i>pointer authentication code (PAC)</i>, a tweakable MAC that can be squeezed into the unused high-order bits of a pointer, and whose key is stored in a register accessible only by software running at a higher privilege level, such as the operating system kernel.<br />
<br />
PA provides three main types of instructions:<br />
<table>
<thead>
<tr><th>Type</th><th style="min-width: 5em;">Examples</th><th>Purpose</th></tr>
</thead>
<tbody>
<tr>
<td>Generate an authenticated pointer</td>
<td><a href="http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc.dui0801g/vco1476202745841.html"><code>pacia</code></a>, <a href="http://infocenter.arm.com/help/topic/com.arm.doc.dui0801g/mpg1476202746202.html"><code>pacibsp</code></a></td>
<td>Generate a short PAC over a pointer and store the PAC into the high-order bits of the pointer.</td>
</tr>
<tr>
<td>Verify an authenticated pointer</td>
<td><a href="http://infocenter.arm.com/help/topic/com.arm.doc.dui0801g/rmi1476202677017.html"><code>autia</code></a>, <a href="http://infocenter.arm.com/help/topic/com.arm.doc.dui0801g/lru1476202677407.html"><code>autibsp</code></a></td>
<td>If the pointer contains a valid PAC for its address, turn it back into a "normal" pointer; otherwise, make the pointer invalid so that the program will crash if the pointer is used.</td>
</tr>
<tr>
<td>Generate a "generic" PAC</td>
<td><a href="http://infocenter.arm.com/help/topic/com.arm.doc.dui0801g/fbm1476202745161.html"><code>pacga</code></a></td>
<td>Generate a 32-bit PAC over the contents of a whole register.</td>
</tr>
</tbody>
</table>
<br />
These instructions combine three values:
<br />
<ol>
<li><b>The value to be authenticated.</b> For all the instructions except <code>pacga</code>, the PAC is computed over the low-order bits that contain the actual pointer data (the high-order bits being reserved for the PAC, a "sign" bit used to determine whether the reserved bits of a verified pointer are set to all zeros or all ones, and an optional address tag).</li>
<li><b>A modifier value.</b> This is used to determine the "context" of a pointer so that an authenticated pointer can't be taken from one place and reused in another (more on this later). There are some special-case instructions that are hard-coded to use e.g. the stack pointer or zero as the modifier. The modifier is used as the "tweak" for the tweakable MAC computation.</li>
<li><b>A key.</b> There are five of these, and which one is used depends on the choice of instruction. This is stored in a register that (on Linux) cannot be accessed from user-space and is set to a random value when the process is started so that authenticated pointers aren't interchangeable between processes.</li>
</ol>
<br />
We are primarily interested in the first two types of instructions, which
store and verify PACs in the high-order bits of a pointer, as illustrated
below. The actual number of bits depends on the configured virtual
address size (39 bits on Linux by default) and whether an address tag is present.
By default, these PACs are 16 bits long on Linux.
<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
</div>
<br />
<figure class="separator" style="clear: both; text-align: center;">
<img imageanchor="1" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjm_bWv_8HvdLZHG34mFbENd062o9P3pX_sw0YpYWl7utdFIpx1rTDkJE8uC8ULxRt-6PQAQzZz7zAckqBr6ocrA18QkPdaa3dngscKtLcGbTrfMK0jg1jX90Rq7vPi1Jbhkgz4xxS8VsM/s1600/pac.png" />
<figcaption><b><code><span style="font-family: "times new roman";">A </span>PAC</code> instruction is used to generate a PAC over an address and store
it in high-order bits of the pointer.</b> The instruction
takes an address and modifier as operands, with the PAC key being stored
in a separate system register that on Linux is configured to be accessible only by the kernel. There are several families of PAC instructions, each of which uses a different key:
<code>PACIA</code> and <code>PACIB</code> for code pointers,
and <code>PACDA</code> and <code>PACDB</code> for data pointers.
</figcaption>
</figure>
<br />
By using these instructions to verify the authenticity of pointers before they are used, we can prevent an attacker from e.g. overwriting return addresses on the stack using a buffer overflow, or overwriting other program values as part of a <a href="https://doi.org/10.1109/SP.2016.62">data-oriented programming</a> attack. When the program returns, it verifies the PAC bits of the return address, causing the program to crash if the return address has been changed. This has been implemented in both <a href="https://gcc.gnu.org/onlinedocs/gcc-8.1.0/gcc/AArch64-Options.html#ndex-mno-pc-relative-literal-loads">GCC</a> and <a href="https://reviews.llvm.org/D49793">LLVM</a> as the <code>-msign-return-address</code> option.<br />
<br />
Much of the difference in security of PA-based protection schemes comes from the choice of modifier. A modifier should be outside the attacker's control, as well as quick and easy to compute from the available data, but if modifiers coincide too often, then this gives an attacker too many opportunities to reuse pointers outside the context that they are meant to be used in.</div>
<div>
<h2>
PARTS: Protecting data pointers with PA</h2>
Modern operating systems have many protections against buffer-overflow-type attacks—e.g. <a href="https://en.wikipedia.org/wiki/W%5EX">W^X</a> and <a href="https://en.wikipedia.org/wiki/Address_space_layout_randomization">ASLR</a>. W^X prevents an attacker from injecting their own code, and is defeated by return-oriented programming, in which return addresses are
overwritten to make the program return to a series of
"gadgets", small pieces of code already present in the program that can be assembled into the attacker's desired functionality. This has encouraged the use of control-flow integrity mechanisms such as <a href="https://en.wikipedia.org/wiki/Shadow_stack">shadow stacks</a>, but even perfect control flow integrity is not enough. <a href="https://doi.org/10.1109/SP.2016.62">Data-oriented programming</a> attacks piggy-back on the program's correct control flow, performing arbitrary computation by manipulating the program's data.<br />
<br />
We have introduced a PA-based scheme, <a href="https://www.usenix.org/conference/usenixsecurity19/presentation/liljestrand">PARTS (Usenix SEC 2019)</a>, which protects against data-oriented programming attacks that depend on pointer manipulation, as well as many control-flow attacks. PARTS prevents a pointer that ostensibly points to one type from being dereferenced into an object of a different type. Since the compiler knows all of the types at compile time, it can select modifiers statically that will be used to generate and verify PACs when an address of an object is put and taken from memory, respectively. This can be used to protect against the misuse of both data pointers as well as function pointers. Verifying the PAC of a function pointer before the pointer is used in an indirect call provides protection for the program's forward control-flow as well.<br />
To protect the backward control-flow (i.e. to prevent the program from jumping to return addresses overwritten by the attacker), the return addresses are also authenticated;
we discuss this in the following sections.
</div>
<div>
<h2>
PA-based return address protection</h2>
PA is not only useful for this kind of "static" protection, where modifiers can be chosen at compile time. Dynamically-selected modifiers can be particularly powerful.<br />
<br />
One of the first uses of Pointer Authentication was to protect return addresses on the stack, since overwriting a return address makes the program jump to a memory address of the attacker's choice.
Including a PAC in the return address will make the jump fail, unless the authenticated return address was previously generated by the program. But this has a problem: if an attacker can use a memory vulnerability to <i>read</i> from the program's memory, then they can obtain authenticated return addresses that will validate correctly, and overwrite the return address with one of these.
This is where the modifier comes into play: if the modifier depends on the path that the program has taken through its call-graph, then the authenticated return pointers from different paths cannot be swapped.<br />
<a href="https://www.qualcomm.com/media/documents/files/whitepaper-pointer-authentication-on-armv8-3.pdf">One way (and the first proposed use of ARM PA)</a> to make this modifier path-dependent is to use the <i>stack pointer</i> as the modifier. Each time a function is called, the stack pointer is reduced in order to make space for stack variables, saved registers, and the return address. Since the value subtracted from the stack pointer depends on the function that has been called, this results in a modifier that depends on the stack layout of a particular path through the program. This approach is illustrated below.<br />
<br />
<br />
<figure class="separator" style="clear: both; text-align: center;">
<img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgWN7PR6M1V9H4heExJW7pM01s1YDfSev6JtK_e3qj3-deHezHO7sq2bUEwTGNp1OytEpDRRZWi968q13F2-Htx1PSrvgcWrQGAZQet0WqCd24dR1tuVjEQlfr0eMRtAd773gLGKYxDKm0/s1600/PA-SP.pdf.png" />
<figcaption>
<b>PA-based return-address protection.</b>
At the beginning of each function, a PAC is generated for the current return address, which can
then be saved on the stack. At the end of the function, the PAC is verified to ensure that
the address has not been tampered with.
</figcaption>
</figure>
<br />
However, the resulting modifier can be predicted from static analysis of the program, so an attacker can find distinct paths through the program that lead to identical stack pointers, allowing their corresponding return addresses to be exchanged.</div>
<div>
<h2>
PACStack: an authenticated call stack</h2>
To overcome this problem, we have developed an alternative technique called <a href="https://doi.org/10.1145/3316781.3322469">PACStack (poster at DAC 2019</a>, <a href="https://arxiv.org/abs/1905.10242">full report on arXiv</a>), which uses chained PACs to bind the current return address to the entire path taken through the call graph.<br />
<br />
Apart from PA, the key feature of the ARM architecture that makes PACStack possible is that after a call and before a return, the return address of the current function is stored in a register, called the Link Register (<code>LR</code>). By ensuring that the current return address is always stored in a register, we prevent an attacker exploiting a buffer overflow from ever overwriting the current (authenticated) return address, but only the <i>next</i> one, which will be loaded into <code>LR</code> when the function returns. By verifying the PAC of the return address kept in a register (and therefore known to be good) using the previous authenticated return address as a modifier implicitly verifies the authenticated return address being loaded from the stack.<br />
<br />
Since the new value of <code>LR</code> also contains a PAC, authenticating the head of this chain of PACs recursively authenticates the entire call stack, providing perfect backward control-flow integrity.<br />
<br />
<br />
<figure class="separator" style="clear: both; text-align: center;">
<img src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjyip61B8p7u4m2U5eIv8_QeZ98bqPxwqG5s4cCxb3ekuAWRNdmY3x5JBemTXyvaMBSR73DHNaqsQwrhDGdBPmSmgciKxMTsJzvEVV-Q_wltm1wwqYqScxUEd1EpOnuARq-ocp4gGwHyr4/s1600/chain.png" />
<figcaption><b>The chain of PACs produced by PACStack.</b> Each PAC is generated using the previous authenticated return address as modifier.</figcaption>
</figure>
<br />
For the attacker, this cryptographic protection means that returning to a
different address is now far more difficult than just overwriting a return
address, as seen below.
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<figure class="separator" style="clear: both; text-align: center;">
<img imageanchor="1" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhx6hLYQJ1k-XNFCjIblBOQlWbV0M9zQKDRqsnAJJ3zvE2dtEGJhHmCwFvJMFunkLAqoGldoFs_LNM3olALpR7FgRwcAMOY14O0Xr3fz4pX_MVeUbFdaf9idxWwm6HowsUkzyJoLfBpEs4/s1600/callgraph.png" />
<figcaption>
<b>Anatomy of a control-flow violation with PACStack in use.</b> In the correct control flow (left),
after a call from A to C, C returns back to A. The goal of the attacker
is to return from C back to some other function B. To do this, they must
replace C's return value by overwriting it on the stack when C calls
some other function ("loader" in the diagram). This new value must pass
two PAC-checks before the program will return to the new pointer.
</figcaption>
</figure>
<br />
A major issue with this type of scheme is that because the PACs are
short—16 bits in this case—and the attacker can use their ability to overwrite variables in memory to influence the path through the call graph, the attacker can take advantage of the birthday
paradox, guiding the program along many different paths through the
call-graph and obtaining colliding PACs after around
<a href="https://en.wikipedia.org/wiki/Birthday_attack">320 attempts on average</a>.
This allows the attacker to call down through the program's call graph and return up a <i>different </i>path, as illustrated in the figure above. <br />
<br />
Not content with this, we have developed a technique that we refer to as <i>PAC masking</i>, which prevents the attacker from exploiting PAC collisions.
PACStack makes a second use of the PA instructions as a pseudo-random
generator, which is used to generate a modifier-dependent masking value that
is XOR-ed with the PAC. This prevents the attacker from recognizing when two
PACs generated with different return values collide, forcing them to risk a guess. The result is that no matter how many authenticated return pointers
the attacker is able to obtain, they cannot successfully change a function's
return address with probability better than one in 65536, making return-to-libc, return-oriented programming, and similar types of attacks extremely unlikely to succeed.
</div>
<div>
<h2>
Next steps</h2>
The <a href="https://community.arm.com/developer/ip-products/processors/b/processors-ip-blog/posts/armv8-a-architecture-2016-additions">specification</a> for
Pointer Authentication leaves it to the implementer to decide how the
PAC is actually implemented. One possibility here is for an implementer
to use something like our PAC masking primitive for all of the
non-generic PACs, but it is not yet clear whether further security
requirements will become apparent in the future.<br />
<br />
Together, these examples show the great flexibility of Pointer
Authentication in ARMv8.3-A. The cost of this flexibility is the need
for thorough cryptographic analysis. However, our experience with
PACStack shows that this is viable for practical systems. This
flexibility is what makes Pointer Authentication especially exciting as
a run-time security feature, enabling compiler-writers to integrate
highly secure run-time protection mechanisms without waiting for
hardware to catch up. As more of these enabling features—e.g.
<a href="https://community.arm.com/developer/ip-products/processors/b/processors-ip-blog/posts/arm-a-profile-architecture-2018-developments-armv85a">memory tagging and branch target
indicators</a>—are deployed in the coming years, new defenses will become possible, and run-time protection schemes will
continue to become faster and more secure.
</div>
Lachlan J Gunnhttp://www.blogger.com/profile/14291915024226566435noreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-23935972902159219882019-01-04T13:28:00.001+02:002019-06-21T06:40:04.094+03:00How to evade hate speech filters with "love"<h2 align="justify" style="line-height: 100%; margin-bottom: 0cm;">
</h2>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<i>It has often been suggested that text classification and machine learning techniques can be used to detect hate speech. Such tools could then be used for automatic content filtering, and perhaps even law enforcement. However, we show that existing datasets are too domain-specific, and the resulting models are easy to circumvent by applying simple automatic changes to the text.</i></div>
<h3 align="justify" style="line-height: 100%; margin-bottom: 0cm;">
</h3>
<h3 align="justify" style="line-height: 100%; margin-bottom: 0cm;">
The problem of hate speech and its detection</h3>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<i>Hate speech is a major problem online, and a number of machine learning solutions have been suggested to combat it.</i></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
The
vast majority of online material consists of natural language text. Along with its undeniable benefits, this is not without negative side-effects.<i> Hate speech</i> is rampant in discussion forums and blogs, and can have <a href="https://www.economist.com/graphic-detail/2018/01/12/in-germany-online-hate-speech-has-real-world-consequences">real</a> <a href="https://www.telegraph.co.uk/science/2018/04/22/cyberbullying-makes-young-people-twice-likely-self-harm-attempt/">world</a> <a href="https://edition.cnn.com/2018/10/24/opinions/bombs-sent-and-hate-speech-ben-ghiat/index.html">consequences</a>. While individual websites can have specific filters to suit their own needs, the general task of hate speech detection involves
many unanswered questions. For example, it is <a href="https://www.nytimes.com/2018/08/09/opinion/if-we-silence-hate-speech-will-we-silence-resistance.html">problematic</a> where we
should draw the line between <a href="https://arxiv.org/abs/1703.04009">hateful and merely offensive </a>material.</div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
</div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
Despite
this, <a href="http://www.aclweb.org/anthology/W17-1101">multiple</a> <a href="https://dl.acm.org/citation.cfm?id=3054223">studies</a> <a href="https://dl.acm.org/citation.cfm?id=3038912.3052591">have</a> <a href="https://link.springer.com/chapter/10.1007/978-3-319-93417-4_48">claimed</a> <a href="http://delivery.acm.org/10.1145/3240000/3232676/a85-fortuna.pdf?ip=130.233.96.66&id=3232676&acc=ACTIVE%20SERVICE&key=74A0E95D84AAE420%2E1D2200B0A299939C%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1545218956_13c31b062c98c57b2385e1b62e5f9cf1">success</a> at detecting hate speech
using state-of-the-art <i>natural language processing</i> (NLP) and
<i>machine learning</i> (ML) methods. All these studies involve
<i>supervised learning</i>, where a ML model is first trained with
data labeled by humans, and then tested on new data which it has not
seen during training. The more accurately the trained model manages
to classify unseen data, the better it is considered.</div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
</div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
While
a number of labeled <a href="https://github.com/ewulczyn/wiki-detox/">hate</a> <a href="https://github.com/t-davidson/hate-speech-and-offensive-language">speech</a> <a href="https://github.com/zeerakw/hatespeech">datasets</a> exist, most of them are
relatively small, containing just some thousands of hate speech
examples. So far, the largest dataset has been drawn from <a href="https://github.com/ewulczyn/wiki-detox/">Wikipedia edit comments</a>, and contains around 13 000 hateful sentences. In the
world of ML, these are still small numbers. Other datasets are typically taken from Twitter. They may also differ
in the type of hate speech they focus on, such as racism, sexism, or
personal attacks. </div>
<br />
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
Google
has also developed its own “toxic speech” detection system,
called Perspective. While the training data and model
architecture are unavailable to the public, Google provides black-box access via an <a href="https://www.perspectiveapi.com/">online UI</a>.<br />
<br />
We wanted to compare existing solutions with one another, and
test how resistant they are against possible attacks. Both issues are
crucial for determining how well suggested models could actually
fare in the real world. </div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<h3>
<style type="text/css">p { margin-bottom: 0.25cm; line-height: 120%; }a:link { }</style> Applying proposed classifiers to multiple datasets</h3>
<br />
<i>Datasets are too small and specific for models to scale beyond their own training domain.</i><br />
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
Most prior studies have not compared their approach with
alternatives. In contrast, we wanted to test how <i>all</i>
state-of-the-art models perform on <i>all</i> state-of-the-art
datasets. We gathered up <i>five datasets and five model architectures,</i>
seven combinations of which had been presented in resent academic research.</div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
The
model architectures differed in <i>input features</i>
and the <i>ML-algorithm</i>. They either looked at characters
and character sequences, or alternatively at entire words and/or word
sequences. Some models used simple probabilistic ML
algorithms (such as logistic regression or a multilayered perceptron
network), while others used state-of-the-art deep neural networks
(DNNs). More details can be found in our <a href="https://arxiv.org/abs/1808.09115">paper</a>.</div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
To
begin with, we were interested in understanding how ML-algorithms
differ in performance when trained and tested on the <i>same</i> type of data. This would give us some clue as to what kinds of properties
might be most relevant for classifying something as hate speech. For
example, if character frequencies were sufficient, simple
character-based models would suffice. In contrast, if complex
word-relations are needed, recurrent neural network DNN-models (like
LSTMs or CNNs) would fare better.</div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
We
took all four two-class model architectures, and trained them on all
four two-class datasets, yielding eight models in total. Next, we
took the test sets from each dataset, and applied the models to
those. The test sets were always distinct from the training set, but
derived from the same dataset. We show the results in the two figures below (datasets used in the original studies are written in bold.)</div>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><br /></td></tr>
</tbody></table>
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="about:invalid#zClosurez" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img alt="" border="0" height="142" 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" 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<tr><td class="tr-caption" style="text-align: center;">Performance of ML-algorithms on different datasets (F1-score)</td></tr>
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<tr><td style="text-align: center;"><a href="about:invalid#zClosurez" imageanchor="1" style="clear: right; margin-bottom: 1em; margin-left: auto; margin-right: auto;"><img alt="" border="0" height="153" 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" style="cursor: move;" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Performance of models with different test sets (F1-score)</td></tr>
</tbody></table>
<br />
Our
results were surprising on two fronts. First, <i>all models trained
on the same dataset performed similarily</i>. In
particular, there was no major difference between using a simple
probabilistic classifier (logistic regression: LR) that looked at
character sequences, or using a complex deep neural network (DNN)
that looked at long word sequences.<br />
<br />
Second, <i>no model
performed well outside of its training domain</i>: models
performed well only on the test set that was taken from the same
dataset type that they were trained on.</div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
</div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
Our
results indicate that <i>training
data was more important in determining performance than model
architecture</i>. We take the main relevance of this finding to be that focus should be on collecting and labeling
better datasets, not only on refining the details of the learning
algorithms. Without proper training data, even the most sophisticated
algorithms can do very little.</div>
<style type="text/css">p { margin-bottom: 0.25cm; line-height: 120%; }a:link { }</style><br />
<style type="text/css">p { margin-bottom: 0.25cm; line-height: 120%; }a:link</style><br />
<h3>
<style type="text/css">p { margin-bottom: 0.25cm; line-height: 120%; }a:link { </style>Attacking the classifiers</h3>
<br />
<i>Hate speech classifiers can be fooled by simple automatic text transformation methods.</i><br />
<br />
<h3>
</h3>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
Hate speech can be considered an adversarial setting, with the speakers attacking the people their text targets.
However, if automatic measures are used for filtering, these classifiers might also be attack targets. In particular, these could be circumvented by <i>text
transformation</i>. We wanted to find out whether this is feasible in practice.</div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
Text
transformation involves changing words and/or characters in the text
with the intent of altering some property while retaining everything else. For evading hate speech
detection, this property is the classification the detector makes. </div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
We
experimented with three transformation types, and two variants of
each. The types were:</div>
<ol>
<li>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<b>word-internal changes</b>:<br />
<div style="margin-left: 40px;">
typos (<i>I htae
you</i>) </div>
<div style="margin-left: 40px;">
leetspeak (<i>1 h4te y0u</i>)</div>
</div>
</li>
<li>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<b>word boundary changes</b>:<br />
<div style="margin-left: 40px;">
adding whitespace (<i>I ha te you</i>)</div>
<div style="margin-left: 40px;">
deleting whitespace (<i>Ihateyou</i>)</div>
</div>
</li>
<li>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<b>word appending</b>:<br />
<div style="margin-left: 40px;">
random words (<i>I hate you dog cat...</i>)</div>
<div style="margin-left: 40px;">
"non-hateful" words (<i>I hate you good nice...</i>)</div>
</div>
</li>
</ol>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;">Details
of all our experiments are in our <a href="https://arxiv.org/abs/1808.09115">paper.</a></span><span style="font-style: normal;">
Here, we summarize </span><span style="font-style: normal;">t</span><span style="font-style: normal;">hree</span><span style="font-style: normal;">
main results.</span><br />
<ul>
<li><span style="font-style: normal;"><i>C</i></span><i>haracter-based models were
significantly more robust</i><span style="font-style: normal;">
against word-internal and word boundary transformations.</span></li>
<li><i>Deleting whitespace completely broke all word-based models</i><span style="font-style: normal;">,
regardless of </span><span style="font-style: normal;">their
</span><span style="font-style: normal;">complexity.</span></li>
<li><i>Word
appending </i><i>systematically </i><i>hindered the performance of
all models</i><span style="font-style: normal;">. </span><span style="font-style: normal;">Further,
while random words did not fare as well as “non-hateful” words
from the training set, the difference was relatively minor. This
indicates that the word appending attack is feasible even in a
black-box </span><span style="font-style: normal;">setting.</span></li>
</ul>
</div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;">The
first two results are caused by a fundamental problem with </span><span style="font-style: normal;">all
</span><span style="font-style: normal;">word-based NLP </span><span style="font-style: normal;">methods</span>:<span style="font-style: normal;"> </span><span style="font-style: normal;">the </span><span style="font-style: normal;">model
must recognize </span><span style="font-style: normal;">the </span><span style="font-style: normal;">words
based on which it does all further computation. <i>I</i></span><span style="font-style: normal;"><i>f word recognition fails, so does everything else.</i> Deleting all
whitespaces makes the entire sentence look like a </span><i>single
unknown word</i><span style="font-style: normal;">, and the model can
do nothing useful with that. </span><span style="font-style: normal;">Character-models,
in contrast, retain the majority of original features, and thus take
less damage.</span></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
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<span style="font-style: normal;">W</span><span style="font-style: normal;">ord
appending attacks </span><span style="font-style: normal;">take
advantage of </span><span style="font-style: normal;">the fact that
all suggested approache</span><span style="font-style: normal;">s</span><span style="font-style: normal;"> treat the detection task as </span><i>classification</i><span style="font-style: normal;">.
</span><span style="font-style: normal;">The classifier makes a
probabilistic decision of whether the text is more dominantly
hateful or non-hateful, and simply </span><span style="font-style: normal;">including</span><span style="font-style: normal;">
irrelevant non-hateful material will bias this decision to the latter
side. </span><span style="font-style: normal;">This is obviously not
what we want from a hate speech classifier, as the status of hate
speech should not be affected by </span><span style="font-style: normal;">such
additions. </span><span style="font-style: normal;">Crucially, this
problem will not go away simply by using more complex model
architectures; it is built into the very nature of </span><span style="font-style: normal;">probabilistic
</span><span style="font-style: normal;">classification. </span><span style="font-style: normal;">Avoiding
it requires re-thinking the problem from a novel perspective, perhaps
by re-conceptualizing hate speech detection as </span><i>anomaly
detection</i><span style="font-style: normal;"> rather than
classification.</span></div>
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<h3 align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;">The "love" attack</span><span style="font-style: normal;"> </span></h3>
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<span style="font-style: normal;"><i>Deleting whitespaces and adding "love" broke all word-based classifiers</i>.</span></div>
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<span style="font-style: normal;">
</span></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;">B</span><span style="font-style: normal;">ased
on our experimental results, we devised an attack that uses two
of our most effective transformation techniques: whitespace deletion
and word appending. </span><span style="font-style: normal;">Here,
instead of adding multiple innocuous words, we only add one: “</span><i>love</i><span style="font-style: normal;">”.
</span><span style="font-style: normal;">This attack completely broke
all word-based models we tested, as well as severely hindered
character-based models. </span><span style="font-style: normal;">However,
character-models were much more resistant </span><span style="font-style: normal;">against
it, </span><span style="font-style: normal;">for reasons </span><span style="font-style: normal;">we
</span><span style="font-style: normal;">discussed </span><span style="font-style: normal;">above</span><span style="font-style: normal;">. </span><span style="font-style: normal;">(Take a look at our </span><span style="font-style: normal;"><span style="font-style: normal;"><a href="https://arxiv.org/abs/1808.09115">paper</a></span> for more details on models and datasets.)</span></div>
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<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="about:invalid#zClosurez" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img alt="" border="0" height="148" 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" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><div style="line-height: 100%; margin-bottom: 0.21cm; margin-top: 0.21cm;">
<span style="font-size: small;"><i>The "love" attack
applied to seven hate speech classifiers</i></span></div>
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</tbody></table>
<h3>
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<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;">We
</span><span style="font-style: normal;">also </span><span style="font-style: normal;">applied
the “love” attack to Google Perspective, </span><span style="font-style: normal;">with
comparable results </span><span style="font-style: normal;">on all
example sentences</span><span style="font-style: normal;">. </span><span style="font-style: normal;">This
indicates that the model is word-based.</span><span style="font-style: normal;"> </span><span style="font-style: normal;"><span style="font-style: normal;"></span><span style="font-style: normal;">To
improve readability</span><span style="font-style: normal;">, </span>the attacker can use alternative methods of
indicating word boundaries</span><span style="font-style: normal;">, such as
</span><span style="font-style: normal;">C</span><span style="font-style: normal;">amel</span><span style="font-style: normal;">F</span><span style="font-style: normal;">ont.</span></div>
<h3 align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;"> </span></h3>
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" 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PjUqVP4sHfvXtohXDLAERT7oz/6I26CaVE91FmaaWinyLZH5vY3Wi7JJVx0sc5JPkk4lz+7Zs9qPm/JlFe0Y+fdIfngKf7DP/xD9JpamA+6ypCbf4tuvdey4uICSu6unE/43ABe3Km76BkpxhjLaf+ijbizBpDhypUryRRMY4c5DOpI15KEL1++jDm/efNmYpI09LljOcgqPq9YsQKklFlYWFhYWFhYWFwPv2W8WWhdzWbz5MmTUMuKoX2gnEH5gwq7Zs0aFCjqwcxofr29vcuXLz937hwuhBqdE4Z5p9BYx/ABVQxYS4JsVpXriSRKHOV7TKuATkXHkHWZrxJnZq414UwE1UaLzZV41dPk0mWeqxkm2UL01kyRckttL2V0n5wnkPoLSoYG4C81Bm3L1dZ84xxA9ICigOTOrtA7wZSgOpOyjkogAdpBR86N+Uav3LhX9ADM7Ypk/spZDSv47uI4PkPIOWsljR/K8ejoKCqEvrvIbxasmFoCnkmtQj0Qvli4N5UslmSzYhlFzJcS0Bh0igxW+aDjCOSQ6/dEpVA/OBJ5n+JGJKhcwlDuiVyRup/fHVKivZEs40J0d+K36DW+kgDJ2RItobhT5Fyae8PivtPT02QcnpiYAK8mtknuoMLEyyFyRUwS3QR/IDaOAmhezs+p5SifGwBzykTTBv3Nt/+R6yx9xSU0B5hhUHmsKeIzOW1G8/AVB1EYrSVbJS0ZQLYYTYgRfaFUQOhIZ2cnyuMvM4SclglwqsjK8PfSpUvd3d2oMF8fQYWNRoPcaImniWxLJ4kXt8OokREbF6Jy3JplnBNXoQAuR3n6gCP4THJDVeS1S6tFLKN5RLBZYTkgbyeumpqaIoJKqyQkcMwWVIsLIcDcMEu9zr1/CQ0DtA3HqSQxedSJjqAxaCRq6zAgCeBgzjNR4dWrV3FVT08Pub5TT2nyoOahoSE8IHhweGGnMU2AfPMtAJKJ8qg5X0SzsLCwsLCwsLC4Fr99XhOocaCLq1atGh4eznVKUmHBbR5//HFmmFJuMSOLCsrj6+uvvw52Ojg4SMr3PPlBwTYDnUzYTMCmAjanvNlOpxS1Q89VTTaVsEBok6c7xcb8UqWrs88ruzNiZopNlVgoWHeJlVKn2cIOQW6smCI7qBbGlmRGhX3jjTegv5K35KABdGjiAHQJKaNnzpyBagudeMWKFSDMGzduJG347NmzkAZp+Ti+bt066P1QSU+fPg02SEo27eFcagBNOncbRjHwBCjBICGQBsrg7pBqf38/KkTNtKNs9erVUIVJyyc7D3RisDWQTDAEkjP9RSUXL16ESo160Dy0/OjRo2BTW7duRbPJfRR9OXz4MM7iRhgv0qHHxsYwNNC20S+0By1H9/fu3UsBn6CRnzx5EsdRCa6iwQUJ+fDDD3G7nTt3Qnq4BLfbsmULCpDFCXWiAEquX78eNZPlDT1677330NNdu3YRLyLJ50T61Vdffemll1AbhANpo80Q2l133XXPPfdAvGSLo7+43dtvv/3KK68cP36c4rvceeedt956Kw0NhIBTGFyIEUOAArjpfffd97WvfQ1DALH89Kc/xcDhLrg1JPzoo4/efffdkCd4FNqAazE0kBW+YgjQ/m9+85tr165FY3LXaGKPaC169MEHH6CnmzdvvtWArHxk4T9x4sQ777yzf/9+3A68CPfCjb785S/jXmjJm2+++V/+y3/567/+a+rpF7/4xcceewzV0pF7DNCY5557DmP37//9v8dzxAy5RcNee+2173//++jU7//+7xPfw8SATN566y3cDtKGKG6//fY77rgDA4ev6A46/vzzz+Mshg8NAM/E7W655RZUS5wKA43uQ2hoNrF00Ko9e/ZAths2bACRI7KHer73ve+hg+gLWohrcyti0WKP/h47dgztP3DgAMYUBXbs2AFRY0IShcbYvfDCCwcPHsTswnzGLR544IHt27eTKz5uhJZgEPE0YS6hNjQb7UGntm3bhgohdvQF8wd9wRHUjC7ntuv8Gcesw5zBc42GYb6hMbgvkWqIEY/DoUOHMD1oGuMS3BFjvXLlSmokRWxCJSiPZzM3pFtYWFhYWFhYWFyL34Zn5jugoLhDn6NYpqzgqAZKc/nyZbKzgcPkG72YcUsjY0Ien2bhbi7FKpy5c0dbBw9NvX9ZXnn17K+b8Uyto+vNs683wmaiZKsZVKsd7VL7w7FTU8mcCuRzp5493XVptbP+ju5b6qzDLfJMPv8xhyzkUaA9ZsQiyL4KrRqqJHRZaNLQ+FUWB6VsgM+k+0L1zLfYMWMmAmh/JhE/ZuyNV65cATEgegklFUdQPz6ALEHfpW2No6OjRAtxIRkYyTJGmjp4FJRsVEj6cbEjkD+RSTJm5vsMcZCStVAEWjLVgi9BOUYzaDkAN0W16BEOkqpNeVAwdmgtbgeNH2WIFuaOlCCNIAwoT/tRSYMfM4BYIEaMO/oCPpOH8EGduBG+kgcvGawgE4iCQt3SWgNp7TSFKOoSOAC4AQSFDkL1xy1++ctfgkGBgYCfkHBw8Ne//jX4ACgZOoImoQB4F1pOAahwHFwIVaF3ECD6hZti7NAAHP/xj3+MArgK10ImRNfJqIsxOnLkCKgjRhmcGU3CQECMf/EXf/H444+jDZj2RHTPnDkDkgMGiBo2bdrEjD856CvKgy6iThQDt0SBd999FzVjCMjCj/tCtpADeoditHKBG6FmCJPcSkG90AAwQDJIYhDRQSJ+ZM3DXEUDwL7A9NBl4ki40c9+9jMUIEFBSk8//TRmCwgYMUm6loYJVeGSH/7wh/iMJkFKzLB31An+ttwAlUCkYJ6YFY888ggoNE053A6yRQvXr19fjDxUJJloMKbEd7/7XTQDogBzy+3M+Iu7v//++0899RTqAX1FPRRlB4UxRvv27cNDQbMI7UG/MDlRBk2FKMDt0UKKvwXJoyrIGaOPeiBATJs8yQ3Zb/FQoDDZITEQ+ID24zjuSA87CuMrWotT6BTkSemO8qhOaBIahqFBgWt3XFtYWFhYWFhYWOT4bVQlitoCrQ46GSsEh8y9yKDPvfDCCyiwa9cuaG/k5UiudMxQMpAZcoFjCzdTxSyM3WCOzbw5+95/ef5/uexcaVTm4t5Euq1fXHzml2efj3kYoxLBE4fFvhR9UkWzPz/wdLnx+npv047f3xyzGMzVyZ1DDYd1dIoUxlWa2qS4z5BcW0n7pIQrowYnTpyAtgpVNXeOxVVQeUE5oCjTNi2yHZHnIUgULifqlUdqgfqOklDc6RRuBJ0bNR84cIBcc3FfFANzAwfYtm0bKkExaLHgM7T1C3eBxgwWhOYRE8vTPJIrLIYAbIGcM3FT2kiJv6BPqA1skPb7YRRwazQAp3ALtJl4Pu1YI2ngCAYOlVPsE1R7/vx5nKVLyFsVt0MBkBAcRDE0BtUSRcdxNBXlQRgoLQetLFDkYXxGyXXr1qEetI1oMCRDJsd8MyRJG18pUu7GjRu/8IUvQOC49tChQ7/61a9AV1CGCDN6AZrx1ltv4RQY1Fe/+lWcet3gr/7qr3bu3AlOhSZB4GgtKvn2t7+NdtLWRJQEbwH327t37+/93u+hJHkF4xRRIAAUDhwDbf7Sl76EmQBiA54DAvm9730Px2+//Xa0HxPmnXfe+cUvfoFeoNgdd9yBgQDt/Mu//Msf/OAH4GMYOPT3+eefB0nGMKGRO3bsgEBwIUaHbocmoSpw17vuugtSJR6IwmgPuaqSO+6ePXvQAHBjNIxM4rgQnBbcHoIiCyeuRfdxO3AnVPjQQw/hIMgqroLoMMeop7gcgoWc0QDIFkT9+9//PmjkgAH5GJ89e3ZoaAj09b777sPTCjGCdYO+ouW0JZvWRFADrbDQCPIs5Wnu8oqhBInFMN1zzz33338/rWvgLHF+UEq8K8AY16xZ88QTT6AjuC8eEFzimqy24NgUlvbUqVOY0nilPPnkk5AM2CnmA9p82223YWQhTEgVLfzOd76DEQE3fuyxx/JlINwR/BAy2bJlCz1HNLKYFWCVqAr93b17N0SNZ5Z2WaOdOLvIHZ2oPmSI2xXTmVpYWFhYWFhYWCzCb5M/kxm9rUjAWBYQlfZf4TPtDSvG/eeFtAG8kD0iP6srF1xqz9lw1m1OlhozlbmoomtQvBXXYrNJLk64kpxJwbjQ2UpCRyquAunMsVlcmOi25W2l/Zkom8XP0Vic1J7uDrUSWiZ4CDE9KLsgcqB/ICpOIbsGNMtiCNb5lhujH64txk0hBg69lgwszMRNgfZ85MgRKN/EM0FCIChcCM0YajTtRYTinu/027BhA66i6KO5JZaCkUDIaDBqznc5kj0WzAGfUSdt5COGjGtpAxvaAwqHYuQ9SCOFainHKTkfMhOEBpdDCFDQaRshOQDnFJdlsTfxGWyKdtwRf0YBVAWSQDtOaWEiJ5+0oS6PdlOcSCJLBEKiRhdAd8HxIBlwJMjhv/23//bmm29CyydW+corr4AkfP3rXwdXxO1Q8ze+8Q1cC6ICCgoisXLlSvQCssIlw8PD6BTt62NZ1pZNmzatX78ed2GFzJ80iDQQIBUgP6gcAwSWgrPPPvvsM888s3r16t7eXpQHt0E3v/KVr6ANRPhxX7ARMFLQQtwXPBCEFhOMyHA+iLkwaTsoakNri5F78n3LtEsQHUEXIBYwZNyCDH3gnCD2ILTUBRQDITx37hy1B3XiCCgcKNZ//a//9ejRo2CYqAQEkvgkZEUe7JiT5M+cJ9KkFQcUQ2HcdIsBZWRF1zAc174WWOYeT87bGHfUhoEA8QNfBQ9HO0ns+eIUHrTXXnsNtT3++OP33nsvLkHXMOdB/yC3F198cevWrbTMQcOBxkOkzDgUQHRoDOYGuCiEg/uitYcPHyaLKM7mHgeYpXiWcQntYk1fBMb/nIRAoGbTEhK9Cop9xAOCYSWGDOHTHuBFLgYWFhYWFhYWFhaE34ZnEukiOx7P0jwU1WIKuJLzzGIUDdIyceGi3U0pTWLCZY7PfBVwHnFRE66nIwOxRLk8dpTwE2jhMhE6WWbMk1gkymMB6o6bc0kzYrEyCTITY8NE03QQINPqNBKQ0lk0F0UZJXqTh8okD1Vypm02m5TFJOd4xUituTREIQbvIm86YqcU94WMP9Dar1y5AoWVNu9BxQdDgFoMXRwKNG4NzgZ9Oo/Ti6tIh87rpKAsUMTxgexadJxIPlRqnIKEc5WaeCb1CCo4DkIFx7W4EY0RvoKQoLPT09PdBsyYnVHz6Ogo1H3cBZdfvXoVB8loSVFb0AwQD+jlYDLUQlROBUDtcFNUiJKoHHQaN4KmTu61UPopyM38RLwm+Qe5cZIvK7EXMBCwPggK9ZOXL3nqvv3222hnHs0VvIuEgM7mOSFpiPP1ERxBzRD4q6++isaAqIBMYixoNSSP9ENxg/K4u+jRvn37Dh48uH//flSOqnBfsD70Dm3AHcl6huEGGUYLLxhAbjj12GOPgadRyJ+8fpGFO87NxRTtiVg3bV3OSSmuIi9WcMLdu3dD5iiGiQRR79y5EwQMNaBVoF60/ZVkTgwKZA+3OHPmDMYLbBYCPHbs2MmTJ9F3VEIbfWnrJsmQnlyyVOdDg1ts374dfUH9eQJSnoWTzQeOphxdhQrBJNEwSBjdp07lYYSDIMDMBzG+//77QTVpBYQM3ZDz3/7t3x44cIDM6awQn5beP3iOQPVpCQMX0hoBxrSnpwd9RL/y1xHKYJLgCDmE5+lPaD6g4xQii95IFICKlm+oOxSpCJ8htPPnz+MU5i25TFueaWFhYWFhYWHxUfiNeWbRWAdVD38pwmcxvGQeY5Z2uzlZ0kWcJfsYbX8qRprNeSZjPse/xGXKd5SvWMS48XqV3ImZC/ahuATPlKxpgsdqY6UjE2ibKjBxak3b9J5MfJaGZuaKoCgaM4uBVUmzpwyQxGpElsEvj0xDWRD4wswWKktZwbOMl4v0TrLxUnwgspJBaYZSC4WVjuMvxAixQIxgCBRyhvIo5skqiq7FzFBc6L4gq1B/QY3oFM9CuUIbhoR7DFhmGYNujTpROXgR2AIZISmAE8pQchqyZ27YsAFsirRw8FvcAoWpnbiWtjiCrtBeU3LxRZncEksxSMGsQH5QFZR4FACxoYQfxHLxFzcC66AW5usUuX2YCF6exzKP3AvlHm0GhaMAvGAOzLAIyI3sw0R+cAS3Bh/LY6VSlFRZsGrj66ZNm77+9a8/++yz77//PiQG7rR3715wfrSKCF4eQ5WalwesWrt2LS4hgzMIDAVJAmfDQQr8S0kat27diqrQGKK7OIWv5LVLwVqpv3lE0zx2Ds8iDIlCJlUqCfIDSkkReu6++278paeJtstCVuQFTTI5deoU7ZFGGXwAkSMTNwq8++67L7744tGjR9ESHKEFCHygZ5ZmNQ06zbokAwYa7afITMUXQtGemQuZ3IMpfC75BZAHdb7GRBF9yDOcDN1ENVEJnhHyDmCGEzLD3vN9y2ScJ9cD9BoCR8NwOXFOliX+yec/pgqKQUrFdSKaw2Slx4WYtMzseaZ0nfhKwZZzqyZki8J4WsljuRhN18LCwsLCwsLCYhF+Sz0JehiU+DVr1kCFPX78OEVGZZlWl+cDyMlDnjrv4sWLUH/BAUj1ZwsVU86Eo/+VOCvrf3GZRXHitHBItj0lRaKYAOFUIC2JchKp76LD/HAc5cpjjsdcUniV9pWVDjPxflKzplgcESi/r9Hsc16X+3DmXsF5GsCi2ZZlORuJgYgs82TRn5axBebTYpIMniUqhBihvEIdB9U5ZQB57t69O1eLc59YqgQfwAooCBNF1qGIplQYqjAUaPKrpHtRg8HQDh8+DK5IuRmg4oOM4VpUBcoK9gieifaDC9EOT+KZqIQMgxQIh9xQaVcnrkJhcjIEVaBOUTzPkZERTAxmwsmg5Pbt29GAqwagHMS7KL1EnjSCqHLuN5uvTeR2M7I+kXiJ8xMnQcv/6I/+CKSOzMX5qgEF/kUBCjpVNAgTd8LZL33pS+CW+/fvf/vtt//6r//6mWeeefjhh7/2ta9Rd8iIWkzSmB/k2cY/MoBjFP78z/981apVFNiGeAtxe2ZsvHmb8w7m+5mpPJlMaa9mfrs8dQrNTBxHhTt27AA3BsOEbEEXMQ1wX7oRbZVE5SBCf/InfwIinYcgJgJJrAlE/T//5/+MI+jsnj17MKBgy9///vcpvBArGFrzuUqSZ1k62SJjv/ZRym3IzGyDxEAQL1WFpD4Ux4hldmwa6Nz8SIs+JIqcx+a+uLkAUTMNUL4EgyO0/Zjy3BIo0g8uwWNFaweoGdI7dOjQ+fPnMe3RSIgRIqWZMzExQbZfHN+1a9fq1avBaSnfDKoaHBwkL3RrybSwsLCwsLCw+Bj8ljwTehhIAlgH6bsgEmQnIUUc+i6ls8sVaGJi0B1BpaDSgUTRJsAiUtVNKkcoBhWXg1YKnmjTJtiiwkclpNuKlSzHvhJOwpnijIOH4oNmktxNBDgB1P8kb6exYaoseabesfkRVJOQU9884WFuvSQOUNxxyrKdhEXPxuJZnMrZES4kmxX0WtA8p5BTVJisjJSfE2KBiFAG7I6CoLKFHIyZgD0gbGgV9N2crtNZjAW0ZFxF5qDcToizYK1gsMQPyasQ1y5ZsoSiE9F4gV9ROkTqEegKCAx0cTSJotGQEy8OohJynUWzKUwuDT0KkAsi6O6lS5fQHgiNIgbhCPgMWgVlHZeQL2LOwIs8kFg9cYzcmkSGQYqRiwaQyQuV0F7TpUuXgt9ykxITTcolRoGCmTFLkp2K6BPR8i4DMPCdO3f+4he/AAF77bXXbr31Vhyk7C/UjKKPKOo/ffo0hokEBamSHHB8/fr1mCT4Sl2jjY64EcYRBSBkjBrILSVfzXPqMOOeiluQrPLnpWhXpPlDTteYGxgCVPXee++99dZbZJilhR5ykEb9ykR4AkGi/ag00ygcFxnl5ubm7rvvvkceeYTCAqE7+IB+0fTOl4fQGMpfQn1HDZiW+EqxfPJXAVu0WpQtxNCcxGBREFdKsspMlFpaX+jp6cH4YjZiYuAsZiONC/qIDkKSuDY3G1L8ody0S5XjKhyhlRFusnHmLDd3TkblmDm4NShibu2EZCAf8rKmKLsU53lsbAx/MSUoEjK5yKIqkhtJuJge1sLCwsLCwsLC4rr47eMA4S/URKiGoBMHDhwgL0FS/qCG7tmzB+oatGqWEUgo4h988AHUNYppSar2NTYByXjEWSN2p6Uz57DI5UnAWcxZCaRPRI0K2CXraAVCuYnLJRelJPHAP7WdUzmx4ALkJPWOJXppULgLT4PQFm2MZDiaL5Kdyq2aZCeh1AjkqkqUAL0gb0MKGkTOkHQqz9gBpRkaKu39gx48OjoKIYAXEYMiGkOGUyi1qBB3AbVDVRTCh0L+4FqKRAqMG5A9kPaJsUynx1iQI2LOP9NhNsZJCp1y5MgRDA2GAA2gOJxnzpzBHXEWrIkMPlQb2gAuhyOolgxTFBwF14I2UEZ7CtXLChQdijjlDv3www/RXxSgiDL4AJaIe6HyFStW4GA+AchHkTJJ5FGXMGHAmck9mJvMFocPH0YNZP6lDY1oMCR58ODBdevWoYVk46J9m2gVBAtpSIPcIkrSwBGMC6hdhwGoGhp89OhRNA8iylkuBIU2oKeUYhSfwUXx9ZZbbsEkZ4ZEYZIfO3YMlA9UbfPmzRRBF8SGeo16KHgvuDraCQKTW8iJTBLdBRdCAUwMiI7CvULOFPkWX9HT3BAK4YMVo/BTTz2FoURL7rzzTrLpkd8pmoExxSMJmezduxf1UDQmyIQ8tPGVwkEBREEp105uFaShRC8wpdFZmtiUPRVdQKs2btyYb3Mter8zk+QGMsRNKeoSudpCVpAb/qKpNEzkjYyq1q5dC+FjWEGbKVcQ+o7ZiK8QC3pHpJ0MmEWTPq3a5IHHaN5SM2h9B5/JlI3ZSyOVr23R4BKZBLtGa3fs2IGzeE5p0mJMKdMvLiexYMJjwpDD//zbylo1LSwsLCwsLCw+Ar/N/kyexcCEPrd+/XqoZVAKobFt376dXARBTu677z6WOcURbTh16hSKUbYA0tWK1I6lOmLCeBCzAIooVwnnxvOWs0TvvIwUh6apqWPoglVqZqb0/kycdpzY4bEbiUTxSDBXGBdcZuyZRDVBLRNziC+4XUEQWbCivHe5zYS0WHQQ5AEsEco6lcQRSteOYmSsI/9GlCcGCDWX9ltS3BSy6UGvhVoPvZboGeqEao6zZDmBTg/yQ+kNhcnXB3J1/PhxnAW7AKeCaktJOHF5nlaBTFXk8ocC4CrkwFncopa3Cjo9SC/K4AiRDYrQQyF8iINRtWgALkFLyMiDYSVSjQ/ku4t2glmRGyHLAngSv8KNoJqjPJkZUQl4JiqB0g9yiAbkPBPtREno9ytXrgQ1wily2oQcwFhefvll8n09ceLECy+8gJY88sgjW7duZcYMuGvXLnQHHA8SwNCAHqBakvZjjz2Gu9Pcy0c532NJg4I6MYLoNeXtAAeDNMhpluyKqHP//v1gShs2bECZ999/H21AtXfffXceY+n+++8HSXvllVfQQXSEpgdGGR1HMcgBI/Xkk0/+9Kc//fGPf4xK9u3bh7vgA2WwRHkMKz0db7zxBgrjvqh89erVzLjCkkc3xW6FWCBb9Br0+9VXX8URtG3Lli2UY4PoNLgcZhHagyNoD8WwpRF8+OGHcS88gKgW8wpMFVWhDegm5SiiEVQmsis+QESvv/46ZQrFHT/44APwTMp0sohf5UKGfNBTiOj3f//377rrLtSD1wKk8atf/QqVQMKUphIDgalFoWUfeughSIZ8szGy586de+eddyCNBx98EKdoByZuR/Fd89sRGyfhoFqKusyyrdR5BDI8KaiZhpUZ8pzHi0IxEHt8gMzRYFqVoPBmeUwgYrmU0BWnaNMvK2yWtrCwsLCwsLCwuC5Snrk4jAfterwe5k9wB9f0DwytWx85rn/s+MnzFy5B64UWu2TJoEO0TfMonXuAlPily1bg7PDwMN0M6h653OX3jpkKWNRkSUlU4zmo224bLXEYSsSOQulymDYidFgspOaZDthjyQ1KVdHR9qJZ1oCC7LEKU9ACHS4k/h8bw6YycYPwyRPa/kkdnWu0wihptgL81fZPxcFb8RkfcDDW2z91O0vl6oaNm0EGRkZGXnzp19BE6/U6OHb/gBgYXLJx0xZ08NTps2fOnodqC7UV6nJd4kIP/1DDhYuXr1wdhQQoHybKL1m6XOd/V6zVDkdGx8G+iCTgLxRZ3Av0ABd60Mglm51rop52EOkuCDE6NjExOb15yzbPL5PoUAwfgjDGEODyoSXLuNDJDeks+qIZfKJWrlpzdWRsrnFhfVcPyqCbnud0dHZ3dfeCkGAQVwyvwrByMrgZEfmlyrLlw7hjFEtc7nol1Im/6PXFS1cgPdRTq3dybW7Sl0BWqLavf7Cr+zLGvX+gigIJkVDHw2fIEJdTCzWlwSC5bqPZhhympmdxCj3EQXS2Uq2jR+/uP7D/vYOgCuBUIDzf+Oa3H3zokf6BwTiRrst37d6Lant6+8F/vvNXfwOmka904FQdPNPxZmYbNMR6cB3HpF9luOOly1d/9OOfjo+PU/YLcAnMT53BsqsHPTcDJyATtOSFF19+5tnnQdVAPyCExx9//I477wYzIfHu3XdrtdaB1n5w+Og7775HpkhUCAZ+1933QjKQ8L33PYAyOrPlCy/96pnnyFa5c+fOJ554YnBo6IkvPomegmT+L/+f/5V2e+La9Rs2ocHTM3NoPM1DSEw4emhuv+MuiAVzANMM0wCiK5dLNHsxy9E2jAjaf/nyZXSQrPFoFUgd2lOrM4jrX/3Zn6Mx6NRLL79CvgbgvSCc6DsajNmCOnG7ekcXpu7/8d++A+GAkkGwT3759+65555Nm7c6rh4mjCwEy0UTM4GkgabiKQAfMzUINH7L1u1oDGo+dOgQqqJ0NeDk4KuYbwODgw8/8hha/tZbb6G13//7HzLjsP2lJ78Cnglp4xZ+qYTaIApMQhKFeQcIHMQ/jKOZlgIlcRZl8LzgL0RaKunRn5ya0duVu3tx0CVndcwBoR9wTDl8pn+YGyiJu6AkvpI89TZwh6MXkDZNYNfz6cFRpg3qn/L2tbCwsLCwsLD4/OK38Js1UV4lUVNNSZYuXQpuAz01SRIooydPnqQscyxLUQgFHSRhYGAAxGzVqlWuK7Sufz13M6kNmnp3ZjXs6JcDo41xHjZLMQ5GXqxcKfzYhIcRIJlKuLE2UXKftUV5tlyvdqA7Cfgl9ORIMm66prOcSLJtEj+m+yaJdFCOaxffjRs3gjOQBcPYu3S4lDVr1kAhhr4bQzd1he+7g4ODtK2Leko+eGEYQZeFVo3+goKSkYr8PPMdbvgK5tBqtYhdgAINDQ3hcrQE3A8Hu7q6yJWRfDtRAPdaunQJSRtfwTrQQorOOjIy1mw20QzIk/KCkN4dxzopJd0OnapUSolRkXPZkj1zw4YNKLB69Wrc1DGkBQdRPwaRoqTk4wIOiMvRcZwiJ1U0wDBhXRVauG3bNowvGpbZkcihmmvu2tEBqeIWaLxhcXrOgGBD3YcozAZOcvdNbwaGg3lCJqnsvpoxooO4O7mh4u67du3as2fP8uVLjWDJs9fHjZhJTEpbhcnihxvRhmG0EJdAYmvXrs08MGka6D2xkAZ6TSFnyNn7lltu6evrgeggTBRDU9ELjC9GB11GnXfeeSemcVdXp87lauYGerR9+3bQuePHj1+4cGFycpLSpdL2PxRDmeHhFcKkV122bNmVK1fIhRXVkkUaB8Fvcfbq1at4glYaUJjcRx99lJvkrixzZkaFmDwoj+NoyfDwcCZMRsMN+ePyL37xix9++OH58+eJSKMq3M4xNBvSBoVDvw4cOICz5P6Nr5AhpJS+F4w51/M8PLBkZked+Hz77bfjcnqEKYjuvffei/pN7GL9TsBdnnzySciNfOaN5ZBt2bIZjwx6cfToUXSQEl3SJk90Z82aVV/4whcgXggQDxFuitruu+8+1ICukZwhZIgCQwZZBUHk+x5FRXrsscfQMJO2x8Uco+Q3eJZROeQzM6OjW0PaZE6nOcPS5CuCnJDJs5dWnSAlcminJ4vkiduhVRhf8lSnu7DCy8TCwsLCwsLCwuJa/MY8k1QrrrNQMqKL0GsHBvqeeOILly9fPXPmDFRtKK/Q7CkgEDROqIbgJNAjoR3mV10XDhgmq8csXilW7+7Ze0Gev9K8KFUSy6gMpVcKvSlTGU9akcRCRk6sHCYip88fXFdbU47KvlfizGdO1i+hA8+KNGOKtouqhXZa6KMmJk05MUY39IVpdbMG5pBnByVqXa2W165dDVWbHAtBwFjG8VB+06YNGzduII5E9INl27dwi61bt+b7x8yWz/TuJgRLFUxp06aNpOmC4ZCnJyphhk2tXr0Sajeqxb2aTZ0uEiovmAZkS1Q/inTolImJiVOnTpHxqqOjzrIUlMXOghKjC2gPsVyWkpYa6ArF/1w0LtTxwcF+SsiBy/NTK1Zo39fMLzrtS34Wslq1aiXtwkUXSEpGgGtZwXiOTtFkAKNAN+kguUZSIB8o/f/6X//r/v5eHKRASnStmUVpRNPOzo5bb92Hf/ndI2NuCkMdOhV8+4//+I/RBnylQaGzvb3dt99+6759+wybcvLO5uNCbqjkmvvtb38bjcfQZBsCaROj7jq1v6en68EH77/nnnvoFKHdDjFJaDKgMCSGf4899mjRX5seBwgWjcE/FDYj6OdrBH/wB39gnGYF3T0fx4ceemD37t3oBXWTOkXzBxdCYv39d9x6662L2pMz0qVLlzzxxBPgovQg553KBWjuUsK4gMihnjyzTvEuOLhy5Yo/+7M/QzdJekrxe+656+6774rjxHXTBLBm06y/Y8c2/CsKObtEf6bMsV/+8pfpicsJIckZX5cvX2rG0aUHAaOPg+vXr/0P/+E/lMulvDYIBHQU8qEWXr2q07dSUk00kqYWukL112qVWm2FCQ6UigXNAItmGcOkOhuNxpUrV8hIni1eMAsLCwsLCwsLi4/Hbxlv1izqQ+dWUUQpELRaBk2xVqvRjk3aB0XhSaDnmRg2tNfx46p19L5L3uF27V2yt/5QtVlrNtzZsignLKYkJeCZQlEEVZlwGYtI6s2bsoN11cLuNd5al5WZcsNA+mWdxYQIDad4QDJtABhlrshCbybVmY5AaycCCV7BjD6dMUZmsjJw8y+lJeafyrcm0llW4FHM6NkkiqIen+u7+AdqYXYM5oJNPxG1MMbDVOvV8Y2EGBkZAcM0pD1VjqnBOAV6393dbfZDzt+oYJ8kGw6IWT0/RSVNy1MOmTMBUujpq2l82ut8AtB980sWKd84Aj0+L3DdS1gahInnwgHDIQ5PcVZBD6DlgzWhcmK5OgyxabmRSUoGSJ5kNTWim0+YkUuSuFAY5tkyUq5SbDCJhfpCMWnDMKQNe9QFM2QOlaFiqDmO03oyMqaDxOJ2JsLT/PzJb0FSpdryNmdiUcR5MCPwDKHL4MlUODdNE4UjTpWPfj7lzL7BBc7w+YJFPmGoAXmFpiO6qfmgmwjKDj3FJlVpHn0nHSYqhiEya0npggu+msBXmiXSgBKFzqZf2t98JYU+5zbYfA6gJB03lkzjoWpmS15Vux1ALLT0gA8kcwpUa2rm1EJ0nIIY4YkgflgM25PPSRJ4vmSWn6XlGxTAHJicnMQcMBGtFjzFlnBaWFhYWFhYWHwUfnO/WcO4SFOEuquM6smN1uU6oktzmDSoY86mmOEPcZQUVcnrq2h6wxMXsd/n9u3p2R2ydovpPHghi0sMxBG6O6oQxAhMNUGTzUgWd7Ae16+WWF1ID2dBMpOUZEpudmrqdsv0ljzzoc3VXDI6aQ3YaKm5DqppjSnraOLBqe9kzzQs2sQVWmgzNHJIVXgKnKoMSQ2DPAAJczJbotbp3dQZEiq18Zudl4vrUEQileh4Jw5kODszc/7cuWXLlvX19nqFkrjD5MTE2Oiodkzt7U3I19czjIilvcDoaGKQcSFNtzLaQLYyMi7pv3Leag3JmOCraUtyWytZZWkcpdnKSZfkg14w6poG6Hp1/bnfMtXgGj7As9ng54q+lI25OXTKA11U8065mh+aLcL52HEznOmQpd1JU4YE7YhYhJtxqnLBJBuC/3OfuCIYi2Ogx9xs0tVTzXUTE1IGtUtDw5jph448VWg/3TbtppYz10l4sr6kAlnIJ2nvKx0ghsaICnpOu623IKPZeVNBeLKmpZMqCiMK15QbmdFUngrQzYVD8xDzRNsnVTrr8vYwc1/KSGkKz/NYnX1nbg4tQ/epEiKrxhqfTgDN6bIZSF/JGu6k0XT1QAftNtmBQVlp4aY4FvQ5nycsM1M7RrB0HHfX7wRN9mKKzOx5btVwS3Qnzkbc1e1PxVXyXWO7dsMgmJ6awhRas3p1pVzGrPaytSRUwhStJszPRmYGhVaazMtN+5Wj8aMjIxcvXNi2bVtPd3dx4pEMP37hzMLCwsLCwsLidxa/jd+slDy3R+VqVk4zchpG2hjpbYvsAOTwef0bmIg94K8l4fuOqDAT4ESnx/Q10WDaVmW4k6GOIuhgZfAXl0H1LDPpUoZMVUhqkumw88kzFzkuGoMh2aN4ltchkw6Rz8z8Qpk7is6o0IYpmmvuDZvz51YrYFmeepQpOp1mciDKmoZEXeS4aNidIGMm3RGFW61WT0/PwMBAV1cXy1RezbYDqPRtCq+SRX8tWup43s3cGJj3gvZbFlx5FzSy6A2bU52CgYv+8oIFNzcqpvw5i3abd5kbq6CgXb6mMQsIM4632wHlAsFfsj3mt84dRPMFgjzZZt6FvHmO41MZGhvyDab24HKioDS+uedkziKmpqZwdwiWgqMWh488Oc00nreCGmOdoCNFoRXsyem1uSGUZcy5aPPMW0ImNTCiosFQ07nM+GxG0MmNydmt0xGhv9lNF7eHCmsi54pcCOT1TeL1PL3rGDPKBD2Oi4ZokjCmN9kS8/rJXAxBYYxodmV/eU4yZbZSQ7l/NL81kqQ4wLlHQD5kJCjTKtp+6ebTwPg2azkEAa0RCLJzspQPpxlTMYsofwltvWZpxOyijzSjB42Q95Q6FYaa9uOZWrZsmQ7clSVKYv+Qa4aFhYWFhYWFxe84fhu/2UxVVXkWENIdja7PC55p+iDpbVD4WJa2TswH5bkG2v5CF1MkR9dJc5E4krmabZKel1AOTKj2vmOsS0x6TBiSKbSDp1YHHVbkPqkR01BNY7hLtfz0tDbRxERXKNsKJXjI2TIzHom5i2yeLYM6lN+GylA2Eai8u3btoMSYtZrOgUHqNZHwTKvOyZ4sJiHMdgwqsnRRmg1o1cuWLSmX76zVahSFKLWrCH2vDRvW4SyOFzwVpdGtPbM0kBBhm2fbuWj4/Ma/RWsHRRHRhsk8vlFWPm+1yo3AeW25AM1MocIqo0DMzIoIzcsdaFlGkNCdr371K0888YU8dwtRRFxPkgGFICEbmjQvfyOxhKJPUWaXzOyskVGI+e6RsyUuIQmzdMuo3iQJYf7bf/uvydwHkVJOEYC2iRaZOckZs4aO50Kj4El5pse8fPF2xfyT5KwLskRZUjN2nY4XfSWRUgtzx2AaB7SCZdQOhcGQsx45+SOJg2bJo0TtpAlpxogKoCPpFuKdO7evWjVMEXHIdkqPM2WjMYlq5nPqmPrd3LWV9qNmFSb5M0U1Zys1Do04NYAYIN3F5BSlRQpl8peEGPZ8xNFxk+vFyeek4d66PN3dPKT6+e/srO/bt4fpYE7dVC2l/8mnEPqFvyYhkK6cHt48ZS4lLkI3161bs2TJIJ4sXJiL3eY1sbCwsLCwsLD4ePzGPHNRMvecZJD6RTo3xU0l2kZaaUEn/jhA8YxMwFguY5ZIIV3NIrQK7xCxk0LH8tHMguyTYJOxseaIQhWJjjWr8m2Z5grNBlOqmRqh8paT7pi3kCwt1HhWYIBEM/IyOZsqUoW8DGm0Rk+tkIIuMlyn14UE9DkRpZAzORtBha7OVOINDQ3RHakLRH0p0R9YAfpCOj2aQRo5tY0IWLazVBVvSmfzLixivDmKWnXOUq5bPu8CkT2WrTiQVPP2XEtZ6QMNB2WhJKLIss2WOQ8x+wCT4nDk7aeS1PFC3KBUXHQVSSMf9EXDSqkaKcBs3kja3JiTzHzc82GlaZ+3mRqTJzgtzpPiDMklmbeB4h6TEGgQqRdUIbFoauG1A0RNpT7mlecyxN9ie0hoxQezOMSYSx0dHbQ8RF0r8lWqmaSBOUnTwATXVcXJkN8uX5fJCWdejERHBYpLVPhKcZWpEvprNr7qjrdaLXqyqM3k9px3Mzdf9/f30wNOUyhvGOX+ZdmW2vxvLoGiWCAKyvebTySWLXwwCwsLCwsLCwuLj4BLuma73Sa1m4wtRs9bYGTLdTXSJvPrcw0+/5BTIyqwwNr1DyHBTbUbHXOFcnxH72NTxluWqZipJGWMTDjKSQuy1IyZ3iz9x7PKWEo8jVejOa54upkvb9KiBv9j2n9djlQ8Ai2WSMgi+XwUirfLa8uZA1V4bTNye3LxkvwUtaF4fFGbr9uFf8x4FTt1bfm8C1lEXEYsJS+TtypvYRHFkouaTTXnDPlaoS1qiVo40MURKd7lWiGwjFiqj50qRRkWa762JdfWX+x78Zla1Lz8w8c0u4hiHz++Pbkwr1sPVVKkUnmdvJCzJz+VH/yoClVqrxaLjuQHr53blAr12lYxw8aLD8iicSHkEs5nUb7usGgaLJLYtS0sToB8evwj32kWFhYWFhYWFr+bWGBiyo08+oOYX/5nmSb3aXuLzZNEipOieSEppiL3ezXhRXV0Hy4oMWa2I5NlPoY6YSZzjeGSmCgzTrhUk5Pex8LCwsLCwsLCwsLCwuJTgUs+b+R1Fscx7b+amprq6x9k2Ya33NlPF46TT681SmQcUDqStmiS/yvXiUyU/kCbr/B/lZiQQZRtka4TpiCKiDQIEDcM1ISJ1Vs7uTBhaYSSH90ECwsLCwsLCwsLCwsLi38S0m1vtLEQVLNcLrdarZGREcf1Ozo6yFGWnGnTDVSfpi1QcLI36vCy2Q7M9LjePMVkksYEYuacYwrSJkMpjEusk7VPH9dpJlJjZpJGU9EFuLIbqywsLCwsLCwsLCwsLD4tuGTGzLdiUnSNyclJUE3Qy97eXoqroTI41wlk88lBitRvdtHWJ52lBDRSuSZHoTFXmj1dmoHKrIQyOy+NM63KMjDoTIUq88c1jrcmyOWn2QcLCwsLCwsLCwsLC4vfaVDGBU0joyhiJq4jedLOzDauXr06NjZGBeigdqx1P1WiyYTZRpnTSL3ZUkn81UZM7TRrdl1qB1j9j6VBZXUBxRU3DrHcVMK1p6w2ZpqtnDookDK7NYl1WlhYWFhYWFhYWFhYWHxKcClAP3FICvzjeV5XVxfjTrPZbDQaedYBZvZntmT8qTZIKGk2WXLNDDMm6SjlaMMkPuiDCUgvcyV3QRpdTUlxSQwaKXkWwVKZTI0qSxZiSKbZ26nSSEAWFhYWFhYWFhYWFhYWnw7cMAwrlQrF61cmzyElnOjt7uno6ABxE67OLxLGkSZ1rivVp8oz9TZLycgCyZWxPOK+hmdqFmmsnEKChTJXcQEy6Rl2STTS/NWXEMM0VDOtVuowQuQyawIGWVhYWFhYWFhYWFhYWHw6cCt+iSWaXDomO6WOksMUjoCKea7maaBxOFbyXeJs6tMNouPoULPGAumylCRyk6qEvpDLq9D5MDVj1KF+BMX3wX8dSrVZRGbgdMxZaW5gSabFzYdikqEibBZHCwsLCwuLzx+u+7tvf/Qtbi64eeb06yS1Z4bAKR1thz7/s8Bh1/FtvQ65df6hAv/AFRYWNw/I3WDRwTQEtIWFhYWFhcXnC1JeJ2il/d23uLngxkoWuWX+4Vqllo58lF3FwsLi08P1lzB19GX7PFpYWFhYWHzeoLLUC4sUb2mTwFvcPFhsz8xR/GrN9BYWnwUUf2zsU2lhYWFhYfH5xrXWHfvrb3ETwc1n8D9yA5id3xYW//y4rv8MYP1nLCwsLCwsPn8o6ttFFd36FVrcRHCDILjuiY+ynFieaWHxzw+lc/3wRU8lHbyBrbKwsLCwsLD4NHBdf0OrhFvcXHCPHz++6NAizdXOaQuLzwJ4BpaRTPtsWlhYWFhYfC7heV4xQmf+o29/+i1uIri9vb3XHoUu+1HustZ+YmHxz4/cnrmIZ9rn0cLCwsLC4vMH13VZ4ec+J5n2d9/iJoK7dt2GG90GCwsLCwsLCwsLCwsLi88P3BvdAAsLCwsLCwsLCwsLC4vPFSzPtLCwsLCwsLCwsLCwsPgkYXmmhYWFhYWFhYWFhYWFxScJyzMtLCwsLCwsLCwsLCwsPklYnmlhYWFhYWFhYWFhYWHxScLyTAsLCwsLCwsLCwsLC4tPEpZnWlhYWFhYWFhYWFhYWHySsDzTwsLCwsLCwsLCwsLC4pOE5ZkWFhYWFhYWFhYWFhYWnyQsz7SwsLCwsLCwsLCwsLD4JGF5poWFhYWFhYWFhYWFhcUnCcszLSwsLCwsLCwsLCwsLD5JWJ5pYWFhYWFhYWFhYWFh8UnC8kwLCwsLCwsLCwsLCwuLTxKWZ1pYWFhYWFhYWFhYWFh8krA808LCwsLCwsLCwsLCwuKThOWZFhYWFhYWFhYWFhYWFp8kLM+0sLCwsLCwsLCwsLCw+CRheaaFhYWFhYWFhYWFhYXFJwnLMy0sLCwsLCwsLCwsLCw+SVieaWFhYWFhYWFhYWFhYfFJwvJMCwsLCwsLCwsLCwsLi08SlmfeXJDmn2KMm6+i8Pdjoa+Q+i+Brv7HXGhhYWFhYWFhYWFhYfEbwvLMmwsgmW3GYjNwjvlrKKMSmkYuQMYhiWHqSxfyTPpXLKbygxYWFhYWFhYWFhYWFr89LM+86eAYLigyqsn055RkkqmTmQIyHdyUTMrs72IoSy0tLCwsLCwsLCwsLD5RWJ55c8FVauGQ8fy/SeZVy3IrpWJCms8ON0foL8rw/GxamluuaWFhYWFhYWFhYWHxCcHyzJsJauHuTILITrEFx0XxEv2dM55eKVTB9MkyW6dlmhYWFhYWFhYWFhYWnwgsz7zJoHhKC3MYGinMEScrlJ3PuCMRS8HT8mqhB61Dh21wIAsLCwsLCwsLCwuLTwKWZ95k4AUimFsgzRHDItXCwsaAKbKr8FUVrJYFE2i+vZNCCn06TbewsLCwsLCwsLCw+N2A5Zk3EziTgkVs3qLpmA+CE2VUC4rSX8ds2uRZBhSUdHhaUF5T3MLCwsLCwsLCwsLC4p8OyzNvMmSEMCeJhjNysXjXZvpZmvhAQJJ63JpQQFxHBtJ/iXCK+YC0lnFaWFhYWFhYWFhYWPxTYXnmTQUFPlliiWQy0KzQ0SFjlZSaPzIPh4WruWKslJOmNoldcMmESKmr2SaV5ToAbW7YNBDXhhKysLCwsLCwsLCwsLD4LWB55k0FUMHY2CSdChMx4+CNMdfkUiRMhJpearfaLEsJOKhX5dJ1Eq4MjZSKxbjKYS5RTGHTmVhYWFhYWFhYWFhYfOKwPPNmQ2T8W2ncuFRcRSyJjBUycXjI2IVxdnVkxqtUewfcoRqIpOMzR+A/Uun/eI5mmCrRXNRQTVPP4gBCFhYWFhYWFhYWFhYWvzUsz7ypAEJYogSaiimpmBsxL2ZuwhgY5nibXR5l7x66ePrC5e7+/vUbV69bxQZrrIuzMgPB5D7Gmwyb5EnL08yaN7RLFhYWFhYWFhYWFhafN1ieebPBSSliKJ2YOcQwm4xdHmf7j7L3Prh07Nwlp1Ja0zsQTbDTM9H6Fd6qfraiwiqMgWf6ipVBTx3H5DyRWdpMTTUp5Yl1pLWwsLCwsLCwsLCw+CfC8sybClwmIJhMSRBGoUlmQ7ET59nJC+zoWXXq0szFiXisWa/X6t1urRUzFQStq+2rDfdyb2VlN1vmsw5BQ87dNFAQo0izyvJLCwsLCwsLCwsLC4tPCJZn3kwwGyodZeLMNhM20WCnL7L3DrMPTjZPXW43RVnVVqiOuTm3NSrDTleU6s6lKBy/OntpfHasv94aqA53sV5H2za5Sb6ZQVrvWQsLCwsLCwsLCwuLTwqWZ95oLArAwxcdk+aYoKOSi2klKCfm0fPs7fdmjpyYuTzuzoa1pNTbDlnQZg0h6qIiyn7ixc04jJUbMhZFcXx1bnxibnlXaePSrjU9rKrH3vUMv3SYdOYTcor0vnmUINMC8rJ1Cq3SSVaYdbS1sLCwsLCwsLCwsFgMyzNvKDSXk4xi86iU1kmulNk9mbCEE5mTicM9HAoVuxqzo1fZ8ZPs9Nm585fbYxM8SDzhlRLFlK8vq7hlFTY9yZxICNePI8Z5ueWwSMazUTIxJy5fCg9NiE0r3L4SG2SacJaZcJJQp0zhPGFCcFc3QVEcWpBarnC5kzaQ065OlrNhYammhYWFhYWFhYWFhUURlmfeUHCZ+qySNTFhmr3hoKM4F1x/Ugm+CA6CB8Z4foq9PcLeOcUOHb46Mt4GSRReLyuVJeNxbOowDJUr4UgmzAeueSK+cXDUpuRz7WQ6ia405JyqL6nx1d1suMJ6tDeun+3Y9EAxeQySq3CUOfow5T3JrazEQFODpyWZFhYWFhYWFhYWFhYLYXnmjYQiasml6xiqqZkbKKXDVMx4AuYY6rQlPGZsLmSHj7LXD8wcODs2GTit0PG8LiGqkXTCQIJG6hpY5sXKcydYqWRi0mwasihUpFgjjEMWty5cmag4czO1ud7aik7WV2I1nVuTmX/gqS4Txl1X21Z1pY5pKk8JMTis4JZhWlhYWFhYWFhYWFhcD5Zn3khIJmLtmao5m8NZajfUJ5x2GPFyCd8mY3bwGPvglDx5eu7slZmJ2USUyqVyLxPlVqTaoeSOKHk8SbJKueSKspXI7B9IJhdCcEc738ZgnpLrnZvNOIxmpuZaI13lpb31pT2sh+vcJ1VdGkST8nQqJmNdgSOcQrPx1+HXbC61sLCwsLCwsLCwsLCwPPOGg5vYOok2PMauIGsivnBR8kfn2Nlxdvg8e+PI7LGLjdnIZ7yv1CWSmLfabqJti+CDDjhqrEz2y5T1aZOjNjiar74rdOVMSKaUlEqBOfJACb9cUwnIZjgzE4225eWWWBpUh+psbUfKTT0K/KNUSeiqHe1Ha/5rfGVVoQMWFhYWFhYWFhYWFhZFWJ55IyHMAOhQripKhOTCJ0/aRsymZtirB9ibh8bPTcqxwJ2TnYFTAimNQ6VAHCXnjg4XKzmLYhYHie+TuVFpepn6zWqrI0rjPwnopVCgrzrSjw4u5IJtStdNpB8mUStMZqeCkYB1+0mwrGOowoZqrG58ZSupQ2+k4xPpOo33LZuPTmthYWFhYWFhYWFhYbEIlmfeSHDFHBMGCAQw5E4DDDNhF8bYhVH2zsGpM1eSKxP+bFSei2UguFMR4JKx4sKUjySTgY7U4/nMLztxRFVKXSllHNH1CinpoMN1ZCEhBa4W+BhFsaaLQrncS2QSyGS2GY/PtZOovbTqLevtXtbL+jzNgcuglnFi3GQzk2luw7TGTAsLCwsLCwsLCwuLa2B55o2G1JFcucNC5o212ckr7O0jrUMnJ85fbc22KonqEp4rSqwstN9qs9F2eVnH9BHMdY2vLGdJwsIo9NINlRnS+LCutl+itHDATyOlVJzgO9e2TFdKGYJicuUJRwg3YUoqdXpybnwmuDQjl093rRh0VnewisMqrs+YIbJKUPhZ8RG9sbCwsLCwsLCwsLCwsDzzxkKyJGJCSL1/kr++P/7JS6fPz/AWq4VOZ1KpKelqP1rFRKKtk2XXM5lO9P5Is1tSVwEm6bjkyqr9Ys0xocP3aMdXIaVmpVKHjTXhhsz/wG1VIrWjrRBcs0uzcZOpmPthpWcySVqBGL80fepqY7y/umJLn6+J8Ee0X9nUJhYWFhYWFhYWFhYWC2B55o2FYI6H/8RMNBS7OKYuTbozST0q1RIdr0eHh+WcjIfggdJwSB39R+cpUXrHpNAZM2Pa42n+LuCDiutNn44hgpTxUuhyMi+kc2yavZbcbOxE4ZZyUXnARFOxMI4mYxFq11nwTJGm+lS52dTclzNr3bSwsLCwsLCwsLCwKMLyzBsNru2BYGwJZxOz8UyDyXKV8xJj4HdpzFimg/BIoaP/6FA+GsrXyUc0zTNmTEMgk+vWr7jkFB+Wc711E0zUEEuTtMTUn8b0MRWLSDiJ1D6yjvLihM3EKtDbOE2Ok0I5gyRNmmJ5poWFhYWFhYWFhYVFAZZn3mgoFscs8Q1RlCJJHM8rB8wRZoMlV6kRkRmqqXjGO7mkYD/a4Jml3RRKu8umCTgzNkh7OHVJc20WzAfMEdwxL5WHjzV3MsxROl4oRWA45zy15IXSug027qyFhYWFhYWFhYWFxWJYnnmjIXUwV+abjzKlbTKOuSe5EibrJSMXWVVke5pkGubIhd5kadimYnkOTZmWImqakktOJLGQ+CT3gBX5V0clijmO0BFqRcLzkjznojw1YqaxbS0sLCwsLCwsLCwsLBbC8swbDcE8rj1QtRMqqKX0mXIMIww0x8uMlSYHpkhZnxIc9JGslFKHnjXHQTsVbb+kYpQsU2q3Wa5NnZqzamIozVnBidSaHJspz+Qm/WboKpQv6xZxSfGEioF+ChZWZvNoWlhYWFhYWFhYWFhcC8szbyxixgPHVa62S9YEKzPVmcSMlVjCpeAREyUdJlbnNJGO9Iyfa6K48ZglayKF+DHmTW365LRJ09gwdXygWHNFzk3sWVWwiDIJRslThil5GiTIUYmnQimligVTiVAxM9yV/HbpvxRuyATBtYFmLSwsLCwsLCwsLCyuA8szbyzAH0OQQ9ep6PSXDou1wdH4ympSR2F+uOKxNEF/DC90mJoP3SNSr1hNPcEjYx1gVhNTZWyTxqU2MjZRQyZNDpKMNKa+tbTPU+nEKkJvzOQuU2GiIgVCq9sh3CxEUFqe5zFtfxcnDzkKE8NXaajeVIaMZ2fT8RGFC2LzMZUYz+WvB1AQg88O5kGVRF4dK1bIWDZ8/xj5L7gw93JesC9XCuNiTYlwUoN53h2+oNuLJJB3oXgk6zXPbd10UOS3Vzzf55udWtzmz21kqXlZMZa7J6QOCPOPtcsWiFxmfgcLQnZdW20OXhzpBWMhFtdMjz+nmTx/lmURqhlLPfYX15m2PzFLWFTJgtZ+VN9Z0Qk/PZG9W1Lc9BNAFaSXvxZY1kUKG84LT8TiZ43nZfS6YcJ0THKnMGzsOm8YPRbmByN9oByK8cYXP1xZNSLLsZy+1VWh0kUvqOyOkmZmsdkJlU/rMTmW9Z1FtgaaPeNmWZPlYpnv76I3W1F8i5cyFwhgwZfrHrCwsLCwuPH4XaQKnyU4zKmCIDrKLXEWMCnLkfACzj2RgO/5XOoBSrj5kRZmC2ZiftfT7ZrmsNIbNcH9EhQAr3TcRJidlZpkep4KUVbqLZwiRsHMC1ZTWq0fQO3AOdeomaYMVE7hcDeRcSyZx7mf8QDDLo0qWkob7+G2/CbXCH8jQJUJksBzSiJi3CMPZKUlH4aOV84yxkgnaYOeK6433XJS5pw2/i+Vq9UyTdQxPqGp0I1YidLRuHpsUK+b7aQVBc0pyTQv0sijRdqbQR7VKdULoyhyPd2gKI48t5QkSgmOu7eDds0vo74obuK45pko41KNMqXNBcaQERLSDgUphoaRJpkuKdIjSmYrF7meqkz+GzS1pHmSMmsorj7paAlETJTZvOc2K1xYnFe6IfzmVySNLPRj6igdhUvPCB0sGn8kDkRaIFo8Tuy7jh/hvENSpYy5uDaO9VDqw5m2LWi9Q5rZko+XozIlPSWZSRLHjuuhfGyYARemZplQDYn2oleazTCRcgU9XymetKQ8SA5FndZHhUqXtzBdAz2yZg7nActYRlnndX/dzzQgNtcTTma8RhQndjaZ5YJBVte8YYrrHZ/MyHxyUClLy9kXdVzqTnEVMc/DK1xL1kW/wpg5LkkEbwSPLtf/VKQX/RKfNfBoBMxLh0PRQ8/SKs0zlT4ujh6LBHOHlTAZnHQO4KMorjUoPeTmJSR1mXTDBtrqsYins8jTDygKByoKpfCZUwsxFQXmbezox3B+IUmZ2YLvfmza4LUwSRWrSVZKZ6A+HzFZwoSjeeXnEdTN2yYbaxM0IOe9LJWbTH910hfCPA3Ow6Sn70O54OynMKoWFhYWFr8dLM+8odA/iZ7RwhxjhFSJExouoPUQ/MjqkK6c0mBKrdyBZgqtihpqoNU2FIUaiG+B+aXXilocceiUdKGuwXf0Xk4ea4VDSXzmFHrW1fQzIwn4mRbayVZozgtdIeWxuL+TBax1aS28YOUQpND8LkG6WtgxF27UShJQS5lUXZdIZmaFSLSKp5Q0nCuzAsk8takpSKo7cTAvMZZkltofSEddJNeCvSNVoxaZgPK6M9OlmlfZPFdrsEKke3rLpTJV5rpm/I2uqVz8kWkrF5rSMtKSW9WKzLNwG5ZdyeW86VLrkUYLTMzEM5Z3aYiRg5nFRVEyH43Pz1pGFpWLOFxu4/G4frogIwVV3nVdImaRfra1/qxd2c1k8l39xpZa48/yCS00+sxLcyEPE8I4vetVJXNjuoqnQb2MC8WCwc+uTe1RaebdBafzu8TmJVbMfaT9/Lla/OOycP1AZn/F4jqvLXuzQbHF0kz0Iy98V7A077GB48azTbdU5lX9BgCdE1LqsRdcZBsjkmuFkD6Mcv5rBpnZKBcuOeVXURkNT2RbILL5GDmx1C95/UrHy0uvbXo6hrnUni35utKCqliW2CqDzs8lF98tbeq1gyxNC4RZVkvfk9dZPFhg+laLji98AV5nHllYWFhY3FBYnnmjkZGHfGmXzBEUZtYxeUo0YzHxZPE5cubtBVRME0OdClOrDiUpvSjmkfT1hk79s19SJaHJZcK1VQRMMpEcVMlV0tHXaP1RSr1WHSTCmKdU1f5cfzQwRAH4gMO7vJJjYv1qtTAJpePrTDExa0DZco1vs6YKIlPWtc1HM1SVugwa+x6+8VBoAxepg2Kedy3yJExVdkn+t1qtN86WarGiNa9Wcu642lKiEpk4mmPIKIo932+FUcn3WoEs+yIIwkq5oqvwyXUu8+zlC2vkLGcRPKcxXBTJQFpMFf3/xLy2DXbjcBmnyxKZOYKb9w/5A8YsN4LN++mxop/nvN/dzQ2RrSYk2bqARxq/J8qhKYEnNU6avk9Zg3wd+pmDgEQOrUBp66Q2B+fPKQ1Kbr9MD4nCHRVog4OZILgm92kxlfnh8vl6WGZMN98lhSJLrc3C0BHuMp47b3pm3kjtZTFv5sKYtsxpt2BTzXvOspYV3jILebKeLenhj+OZN8tMSH1llRTGhVUmiXJkrJSTxJy7br3K0ljhscLLBF1XETOEL2FuyDrJ/K+fG1ro4df0nI5wR88gI7rU69os6qh8sBYsBLAwCkv6NcLpcq49Yp3YLHhpcquka94L2veC61UQx9ybF7uUrpeZOZkuSdB46SeauHQ67MYC6yykjGKeKWajfM3vzrXDnzUgX8laUOXNMiUsLCwsfndgeeaNhWBCzuthyriumn/EM4VKDUoJTz9n+16IlMpMi3HczFqmI8tqqwiIhV5E146O2QYho9Fnjk/ZzkBTQ8KJll5jgrAowuxwlUEQeC5LAsarTJsABHNcISMlSlAkDd3U4+Io45ao1TdcmWiDDxekVdFGSHJGDrl2YDYei1ykHqXznMqYAvJB0epalOptqa/ptTvZFlBNpXICoMic6Tuu1lxLeugr5Vo690Ip3NS4XTBELKw5874rsMHUs7dQbt7CplJ7CVVIXFSleq1UnjalurHmwNS7pEAsqV9JxlLzDZzZXq+b1syVQQfZyjTstC+YTqJsnm6z2oDRwMRIMKswYoZ2OcIzD712tXUdb57XUYXGNlkY+0V31PNK6uS8mfezXoHgKpG0/bqo4QtF0y/OXCq1R0PCMj5ThN407mm/ztzTNZ092SsnXUSbn0vz3IMRa1W5s6z6vLCEa5fpDGNz6XEWrnAMedPLRJyFLeb42kFaGJFF2p/WzI1EJ5eKtM+I9mJ1dPRvvnizZc48hSalxv3eSDHBW8LBo5WQ//OCZ1mYnxzpe8YxJklYO9K3pyblHr+0Usa5xO+Jo19qZnIZAstTd3nHuMen7yZagzBlFHOznxXUQi9DTusXC/0WCs4XxZlVXPVYQCOlSJezzHtgwXZhuzRqYWFh8RmF5RU3GMo4oM6bEFT6b9Evp473o7hWBxKjwQvt8xoJ89sr8ZOvRMyVTBpOGHvcLbuRwzpjnBVNJ4oc0E6Oy0XioaSphlJrQomQ2vMR5EG6ev9V0UHO4jrQynzFreATtMOEZ8q3Y7xStbrkxZK7KmaafOoLyNXMoTBOZmeaOeLmDMHJInMYfcnJrHyE6ziqMdLI59X4fJ+SWGAXkhh8vaag3VON9cp1oTtKVwgQiFbMSi6Lg6DslfRVZczAiNQ4ScTCaIHzm5141gxy+OQi36jJinuo+LwjnUzbKMgr2DjjhRwabRQL3RIn4dr6kpFLOc8q087GhXlIDZPX9bC8uZDp+96iQXVMBt30uccTCdKpYkeVEuNSS/ZL8n1VCSQn5kvnBkMldWBp5RJvzKyWGRS9ZGQiQ73eJFyu/4nFRk2z5dqsawVmlcBLt22a8aAaUvKgaKQyqkwLYSmJFCYrEq1bUQQssdDBks1Tzfl75+N9M68jZMGxCodoBUcGrUYFT6Prcb1kINphUC45flU/znESJyJKTBA2LcdIpY+SU3BATT0IyORIG+PNWoyjd32b9SdXv3lkQsOP5y6ef7LE/BtDXynawVyl5DXHRy6eORdECqS0ifdWubxizca+rrLvpJZJqYhJGu9685qT2T5b8ysincwfx/xoObkxVeVl5u2Z9KJbMLh5FLSFMkzlVjgkzOJFfD2JK5UK/To1WVhYWFjcWFieeSOR2XzShV6uWE71kmIxziiKrCOZJ2lXnwyETC0ACtoiL2lFAlpMWe+ZDNrlOdaF0e1iFxJpgnw4jjaWukqZCCQmgA03VNPcV4cC0kZQHSgGxOC6P+cWGpqnuy6bNUSNpQFUBHeSmLXBLitMG+igiquY63Q0ihS+bKeWLPqVGnVMsCxFqkp19ZTsfYzGlDtOm0oWkLEcnGcKoJ49CVmtW9NTle7+ZisRFQcTrKy5Mjn4SodM32zxAscCq0F+jPZvzR/PddnU8FhcJ0n7ojO9xib4DcUe8WcbYaVWdooGrmuEvaDLma59U9u9CnabzIyoIuMqqaOuONUOcwS8QcYJc6teGj6HsyhJSnoeCZWuAGS2rPkRokDVej4S33PyeLYGhqYqJdvGfqWCRlyqleMsR5HD0gCkZuIkxTeQMLzTbBSUWUTi3DMzK6Tm1yIys7zKApDyfNmCLWAPgrbumqs+HyQh3f0gqbPpek26C9FzXO7pYWoHYalUdhztdRzHZrnK0ZzfSd8PxvtVphUUZjvPbsGM2RCFIzwQCUvf1+mLQ6/G4KfCnd8ZSsi9rM09yqVyY3b0uRee+eVPfnbu3PmGNqTW3e7e/8f/6/9ZqawC0STvDaEDyIV6CU2/01x6nSR5c1S+UqAyzwuPZ2+EzGk2b78JTpeaZOXip17J7FVW9F9gud+ECZCb7Sjl+e2pnDAWeoov9TmYRRYWFhafH1ie+RlCasw0n8lmlZhfzsSEAlLG78jXxECGLoUvZFp7M/FmuaMV+PqccEIWzc2puVJ7WaW6mnl1kbgxLtWr5oKMHjpqrYl3qhxprGdgmNKniC43vcHo00bCrpw//1f/v78fnZye5u1Ath0V+045CnzPr99x332PPXr/YKdrtPVI6E2xxlDF40L42MJDJz0dgSlT3RSRBT3csWSJUf3TML+pzqrLpyUlcQOo8mYapEdz/0PKiUrTKVZ6iSJpnTl98hfPfqfFnCf+6Jurly+pgWdKcD+oujEzU0HMb/AjA4LItFQpcgc1niZLKBhuZOZjSQVEFlJIqmyXZio7ipQ71zpx7vKpiWjLzluGeigiiVfw+aS4tSLTOPMzN/3LKpXwomcMJDNp/ujv/vbAkQ/HmTc32y614s5Stbunf8+d967buXfpcM2dTzyDgRVGPHkc0fmKjK7PWb6j0ng4qmzzrOGZsdBe+M3Ri2PHT0/2LV2zYsNyZkxhgvYT6zqkdrjWixOC7PDcaPEhI39bQ0ExD2l5jGc7cGnupWNFJtA45R7G+TOd/YXIwukhzCxHyjQ+kci6wVg28LzAX28GLPQ9SI9IzfzQn2DuuRde/vlzb7Qj4SR6W0NfX9+WXbv23X3XkqEeR2/ebEvtpuqkfN1UluiHyEl3QipaM5p/NPJUxiZClEgZvZ4FsZM6n7vXNhBHy7XK9h2b6z4fG5v48Ni5949enAhlo9GIySVVUshycwe8JWRHPgRJ9qIwfjfzy1O8sLFcsXzVIjNcpw/1YgEVai0smc2XyLZ5p2tXektC4Z2QznCZ++haWFhYWHyWcNOrbp9DKGHib+jgPMzQhtiYL3SgTpn5aqYavF66BoMoRazSZKUZVptJkonGxKWxmTMNtnLAnesd3ObNxV7k6gBCgZCxjhEbkwpiaK3WDJRykzTArF0N/ngoKdujI5deeP75ubaqr+jv7Kt7Iglbc67omp0ab7TmfGPnZGGkN9bxjKeBX3GKBuSmgS8yW7TRs7Mtt5meLjW/jFI3WiUW6mWCTArZo0t7OD0qUyB14I86SrG2XXFBBofZybGpicnJUIJ4RtSG2ETHdVOTizSxphLjWccLHo8UozQlveT9xlMFV84rrvMmKWNhSN1ck3lV0gSMidrTo5cOvv3OgasBq/QO3rYpa25u/zGatI4xIzIvRJnvBb3ZQS7xaZDN3J4p40PvHXjxlbeS3mXd/QMrq7VWY/rEycPjc1PjYXJr+a7lA37CE5Fw4ZhdvvwaVZ0T5YupTtpnmwaPIj9LHT0oMbtwpWxOnzl2+OXXTg2svPqNDd8wlXGekwMzlFJznOw2FHVIbwIUZlpmOVAMBVpkYc54J23qjA21cChoqquuJRlCp1LK7JmL/DhuWqRCzEC5gmgBID575thrr77hV3qXDvSWfOfy1TMTjfHJoL1n397N65dXnYr2Og7TR8lJHzFpkhsLE/Et0WuFFBvc+AYYy6H0GUVvdWnQlN7zHUnzDskI4bwJkKx+rhDDq1YsG+wPm4167d2RsTAYnxGu43p6oKJEeumm/+RaF4LMzV1TZyKQmDCOmXEiCyVlyojUAJ4FyL32KZbzjvdxSp5Tr48Fa2eKZ68n2utdIKL5nLHGTAsLC4vPGizPvMEwcTAE5TlzdNwFiV96aIuR0JZHZn5iI6MmeEpHMm3oBCRKCRe/tDE3foiK9USs7wpzz7ST82Oq0RxkXjQeNM9dnj50acltK+ubOwfWsLlONuahhjjyNMsQgSzzknBKXHra1masnUoxX9pF4Y+BdHzozO0ewOndcc8dDz3+MFeNoN2oVTvbcdS/ZNDzlYpCDGQSSOmUuUfiDKBFxVE5SlgQx10dUAcj1Wwr1gFS13bCckW0Zmcq5U6Hu5FRFxXpaVqLdOcaMpHNWsVjQcC9clLyKcKHduIFSxEimGswp+pXHKiWrSBwPRGHUWetqozfYtxsum7C4rnens6tmzZPxKyru8clzVPT0BYLm8orz7Ya9WpXs6Wt6BWflXS2xjgp1RthWPe5niCRVFEsqn7UaomyFwR6cpbLrB2HOt+rEDKIpSw7FdaWUsmm0MFuvcipep7RlD29JRTtPn/yw5PHjrz2wciK9Ttmtm0oewLtrJdNWBntbxzqesMaLsEMD+KGW2KeXwtj4TvsWpX3pgOeY84TYxRytXMBxrqtuioDg11rujbu/eq3vrF9We3y6YPPvfCz19574/1Lo8s3rOvuXVbT4YC0l7Y0aTQlD0uVSqMR1usllmVJpM2/wUyDdfWGjDVnpjrrnbNN7nmOz5Xnu2HS9oUcv3r55PEjB949vDLgo5Otrp6KNiibPX+sHbCyiBvtsOI6otyca1WdmhOJOcz6HuPPy5Ky7kGLOZU2Bp5hoMMKRrYV+x31Vsh8n5nEuyiLcUyCpvRrfqiSOFYycsslNtOI6l2eWUuTeuMp+lISMT4l2olUct8vOcRd8dV1mZSZx+3NgYUOwOnSiWHUkG8UdtQrfqW697Y77rx137p1S9945Ze/fu31H3zvbyYmpnq6vzo82KdJl6NDV7NYNaOAl7jOQqkch1cwaXQSVN5iczMxd1vlbuVo614JPxFJW/ukhG4zZuVOQ7lkw9EPVrkVtOuul4QJx6OFn5cKWsG4p7dWutWqW/JLvfX+vq6OSjWJJlS2M1STTOJ4MtTLkWlYqDjSEbfdCm6Z6B8sWmiaacyWa73tZqIS4WNSezrwmbaea5O4kYBKVBjgJdOK9eqVL9uO47SVh3eCyAKj6RsELVbpaMeq1Q566n7QCh233MSUrOvVGM9sQo4bUex43PWF3lQeSu6pss4tynWkJCluqrliYWFh8bmH5Zk3EjwPwKhy/TnfZcMTnv7kM8MopM6vp3dV6oyZUlVcXmc61qMYYc5Ios61vavN6pR0ZTl2SiGUvTkVzcrp5mj7TDPa0utt8HtWiepAbU6FM1HieH6iRDvWfEhpjutJzY1U5vNmcV1AYQqEin3fr3b0LBlauXJ4ebWsV+tbrTZ3nVK1VNL7IINf/OTHv379DbfS9ZVv/5927V0Tqvabb7z2q18eazbFk19+7J47ds6Onvvbv/7e6TOy0tXbkJcvXjxWd/jWDbvuve/rW/auBwExKS1jFsVv7v/wrXcOvv3uKzg22FXZsfeW+578Q6/iCx+l9F7d4x8c+d73nunsWbZsePXl0ZETZ46ev3Bm1YrhP/j2H+7esiNqxi/+6rn9b784NnZierrVTvo7Vqy9/ckvdNVLiTZkxhPnj731zptPvfxO98AwlNWZ2WDlpq3t9tTk2fe9snfPV/7o7nvvAhfFPOGq/PKvX3/ljRePnzgB/W7T1j233v7wXXfvqVXK4Blz4yP/2//6l1MNZ/na9dPx2Mnj70WjV5cNrtp977dvu/2OoV4ezk2//94bP/ir/719ZWSi6YzN1H78k5/9/Md/5XExPLj58YcfveueLbU6Z0kwMzH39M9+/trr703PjXs13tNfueX2e2655fElQ1Xnpt6gmcZiAXWK9NIRBQDT60Vx3Iq4dJYuW7th8+ahulwx5PUvcWeT+OUPZk6cPjUw4NaH+oIwfOFXL77+6tsXLpzDjFq5etWGjTsffexLywZqFAcFwrly4vj/9r//Vdi1ZOvOXdHEheNHjp66MN3T0/eHX3vyrrv2nD17/kd/999OvPkCZ/75C9FIKA7+x//IBO/2K/fvu+WPv/pkva/z0oeH/+vffIf39vcPLZm8PHLxxGmGKbFhy61f/urwxpV9HaA/gatjoLpvvL7/+TdfP3/mSDA3uWfz1t1779x976Oeb7bnijgOW2+9/sYPf/zi1p23Ot1dVy9dPX3gaHO2uXbz+v/uz/5k6WCfZhhB8M6br33/uaePnT3tS9Hd3b9xy+577ntg47r11So3AcuMGfbzkKZXgEqrpJXIAFyxq7t/ycrhNRtXrxyudXaVnn7qtYNvv7l1920d3X1DeAEHzbnJ5nMvvfzCO78+e+l0V93ZuGHXA/f9/p69G0LJgpnpc2+9/KOnnu3e9dDtjzy2ZXm3jObk2JV33vnwZ29e7Fqy6skv3b92RV0nOpHBc8+9+uY7h89+eKY1M7dh45odu2//wpe+LEoYc0+y0LBUs2taB52SrvFpyYypZJeMm43G6eNnnn3qreMnzswG4/3LBjfu3PelJ7862FfXS6FJ9MwvfvrTF17t7F8hksrIxRHZmqt2lPbcd+c9Dz2wtK9P595MQtxvcnL6tf0v/vK5V8fHx+tOsmXz5vse+8q6LVt8cntQIJlzf/M33zt88tz6XXd6fvnMkUMnjx+TrHT/g4998Stf7Qd5jvG2Gv3psy+/f/zs+x8c8zxn2UDf7ltu2XPf48MrO0uOzvZ5gwfZwsLCwmIhLM+84Vj80yjTnBCK4s7qcPBc61ku5zpmvHGpxblSi9UbrAKSeXbOvdJ0J2PZ1ruhXAEGyX0Zy0jGAWtMt6Orc+Pn25VT9fLOnvIGx1vh1zp7J1UUOnEipFIJFH1XKJ1fT5lkGDdEDDcLuGi1WnNzc+WarNTK9U4aP9HhVSG3tgkPhPGq1ypha+rIh0dXrdm5atVQlIy+9Oprz718bOP6PZVKJYnbnhcdOXro7XeafctXrt/Z19PbJSfGXn3x2TDsdOr15Zv7Sp4UzdEzxy788ql3Dx4+A1ZZK8nLV85ceOpiWFm557Z7O4fdRHtDJ83G7NvvvClZvX/pqma7EUERnW2cu3QxjGLXY450a7WahwHWBtb25YvnW041aEfaqVEnZZHt2bFTHx56992DO/d2hFPjh4+dGWvKasUNrl6cbs56y97buHvPqo5oYvTS8cMj3//Bjy+NXujsqtc958MP3r96tZHI6K77t3WWEtzg5PGjx86GQ1emu5Z70ALBGI+89+5cPNTR0T9033q3XO3q6+vs7q2HUTts1MqVzo6Ovu66SPiygZU9fUNlX6deHLlyYf/b77/x2oG5mXhgYChgc4eOvH/2ytWZmfpDDzy6avnNyzINVJ5miM9ncOER5oPryEBG7djsvfTqlXI91i6lwnXdkscb7anXn3/15z/75fjI9PCqlWPTI+CfF65O17oH773j/v4+zrXVSQoZHj38/rnw5Pmrk2LmytiVy3NJOYhi0Buw2q6unsG+wcnOzjhkZV90dNYHVq1wfG+o1LN0aGm1rM3sU1MTr7/2Vqvat3Ltut6a7zpqYuLiGy9fapR6Hqo+sWRLv8tDNX3pwP7jf/fTN09eutzTKysV5/2Db585d7kpBvfdsWuwC72LXMFnZ+d+/fJrZ6/OOd2dYFnhlanW7Ex9oDNRCVqbtGb3v/zqS6++cu7cuWXLlrmRmplu/+LpX01MN7/w2KO337KV3C+VBkV+yRwtFmWX/UxhkWMwX/BJqjBRkTbQ+uVKR2e5VC6Xeu7ct+vCkZNPv3Tg5MWxTbvYkBfPjV98/sV3f/7sCxPRbL2r3unHhw+83ZztVW7vhl19FVwqgvffeMEJe3vW79y2vNv3HNluvH/gvV++fmbrraVv1xzHCdXUxIEPDj//4mtnL0x4sarWayOjl1544blqz4Zdezf39lEoAFebnbniOsuNtnA63DWRzcyW8jAAP9z/7oFnn3v51KkJx/E6OztnJsdeeeapknDvfuDxNcPdrifm5mbefecAL59fv3ZHf3eXVO2LZ08987OJamf9trvuWtrRgd+rmfPnXn7nve/+4GchF9VSKQwaLz733GzbuT9QO/ZsrbmGZ8rkzNlTL7z86qmxVrnaOX3lPIujmZmx2cYcpq9eUx29/O5br77+6muXp1pr1m1oNBqXL58f/dWzI23v4Ucf3TJcd9hndVZYWFhY/K7C8swbj3yjFs9SyOn9M0JIisBhgm94ipcTBmIBVRwKAGswZ1yVL7bAMKujgd/UaRqlcrjerenoXXl6Y4wqKafm1PB7PHe6NTc5OXtxzj9V79zdX9vEeJfXrHmBpwLZVPgZj2OfCR9KhmWZ/wDccrlaKVdBpIKgNTYlBZtlCiSh4vm1clmX4KXq/Q/cy8Tcf/n//uWHh977qZP43VMHDp9Ytnrtv/iX/2r79pVCzXEVQdUu1zr7l6x68quPrl1eGj/y/v/y//6Ld958q9I78OTKR+pd3ujYxR/+4LsHDjT7hzb+3jcf7+lQZ4+/9sOfPvPjn/zCrQ5uGN4WRYHjJT29neCup86OxayyZuP6FWsGKlW/p6dr+cphrZ157NZbb92ybmh8/MihD47+/BdHZh1HKR350mRZVRU36Sx7nd19W7du6/FCzJaZRrx52/ZNtyx7+pmfX52cOnP+0tJNtbNnTn7/+788c/byXQ/efdfdt3b4zk9+/PQ7B84+8+zzy9d0bVjVV/V4T0d9tjXSEfFbt2zfu2VQXDr53f/jRwff3c+8jp27l/f31LZs2/N/+78vP/7is0//8vXzx1r33XPvo4/sLTuez+v1sskEE4dnzp742c9/cuZ46+tf/9MvfvmxicbVH//yu6+/dfC99w6uX7d1eMWKm9u2ZVR4k4lBr+2YrZGS8UDFjWpdKC7HZ6aDrvqVM+eefurFyxen6pWe4WXL+nu7pkbOvfLS87OTY7t37vvGt77VTBpPPferXz37xvMv/HqgZ8U9d68X2hOhKZOg5HtzU8G5C1e3LevZ+fDOSu/SUtlbv2F1Erb7evv/9E//bGzv5heefenE+OH1mzZ+7V/9QW9v76BX9SMmSm2WtP2S6OrtGx1Xjt915917Ni3tPvvWq3/z/adf+/VrHUND27c8XGPy1LEPfvSDH58+z3bf88BDD2zqKLVf/NnP39t/8kc//nn/0uHBnb0yabG4jX729A2NjE6zINy6afOWLXu6KpX6EKrvxhy8Ojb6+muvHHjn7frqJX/yJ38yUO05dfLCX3//J6dOnTl44P3Nmzd21vXPk+e7aQCqmwfzEWmUKGY60btg9TvdiaRoRjLSe1bjod6uXetXvvDS2yMzsxNN2Wbjr73y3A9//OuxVvTQk4/s3rU+nj33o+//8u233w1F15+v/+OurvqaVcN7t256u9368MjR5t5VtZKYmJg+e/Fy/4rh9ds2DvTXmZo4+sHhv/vu37/14dU9t933pYce7SiLV1751QuvvPO3f/u3zPvjex9Yx41XMuOGaurd1LHeZZ1IJ2XxOnLYyIWLL7zw4jsHjy5due3xxx/fPNy7/81Xf/j33//VT38Ye52D3/hiNWl1dHV21vsSr3vHnn1ffvSh0vSlp5/68U9efuVXv/hVqat78I7b3Xbj1Zef/9Wr7023g8ef+NK+XTu8YOp73/3+u+++OxuxlWs3lHs9T4cfUD19vcLxDh87uWrN+t279g50dXil8uYtOzu6dJsunj393C+fPn3qyvY7Hvj6t/8ER/a//tLTv3rhnf0HVq/fsHHpds+9XhgmCwsLC4sbB8szbzwWh580f3U6bYozqCOWKi9hlYi5kT7izbDKpdC/2PCuttzZWOgIQQ539J+Yc73FSUodRl9TDB24xldOZ1wLJuLmbBSMzExdks1j1f7b694gk3286VWDqJXIyGGOozV98bkIxfEpQZg8hG4URe9/8P7+c8d/+KvvNeauQPZlv+fBBx6/996HVy/rqSswPv/WfVuOHd391juXfvB3R7xexqrVu++5d+uelb4PWqHT3EnJaj19K9dvXLVu3bJuuZy1bt217Zk3Ro8dO5Uk2rR86eK59w6809d3z0OPPvbQg7f7LhvuV6OTzR++cOns+YuN1rbeisfioNGY5UL09Q3c/+BDX/vWNzv6SsKTzcZsd62bJpNf7+jtXt27VFy8cLmjszYzI3XSjCwmCItmHRnKRAwMLd++1Dm4/+DR2frgkrW7NkU//8UPA8mnW4FMvDMnjpw5c2bn3rsffuyL27atq4qwNT3bCt87dv7CpZGxzRuWy+lRFbV7e5ds33P7gw/fu2nQ85d1nj5wcqQ9OTM912gGXT11zOiSVks769WajOd6u7r7+npKJp2LjoYjtXDRtpmpyXKpzlnIhdy8ZcOqTf/xq5fGpyZLK5YtvZGD/4nABAeWzFdpTFGzsZJHjhOMjJx95/mnXt7/zqpSxOcmRi5fWbd1xx984Rtrh1dUWMyq/je/8qQv6kODqweHlypXXhkfffe9E1euXL1w/oqU6x3BZRI7QlZKXnd31969d//LrzywZuXS6YS7QvZVFKZKknDHq/T3L+nu7OScOw5fsqS/Wi5XMUk8s8VNho5OtstXrNqwY/ft995/x1A9HnanTh4/8fxxMTUx2w7CyA9Pnzp+4eyZ9Rsfe+iRR++5fYXPGk6zwVjt2bfPn790ec/27rJO2uEkSRyFcmDZsru/+OgXH39sBShsrCajadekBIqjVhw0lQwhgnqlunrV6qG+5cPrtk7PtTs7Kh11l/hCoqMPYWLcPOxB8etQHb0hV6cw1llLHbySvUhws0eXizjqrbgOC6fnptsqSuLZQ++/2Zbq1jse/NpXv7F8qM7a68OG+5OnTpw/f35iprW0s+TVavfec9fBF05dOP5BMHMP6yqduDBx9OylTQ/cvXfPJoe1WGvy4rnzp46d3brtrkcf+9ID9+zRLrBqohHFr789Odtohwkzb3tltl22NKtUUr+U4sRJ3wwxE+2Ry+c+/PB4T/+yOx558O5HHxj0ZEm2Js+dePvImXPnLwaclbjEy1Bwb/nyteu2bFuxuqc054IDHzp15ujJs2NT0zq2z9zkiWMf4jWBCfXwFx5fNzzkN6cbs3NTP3z+2PGTM7PNwZ4uLR8V4xfIr1bWD2/9/T/+08fu3B3MzqFBpXKNAhJFUaPZmOQsrld9v+ytWbNk28Y/vuXOu0fCyvLhIdczQnZu7mUoCwsLi88ZLM+8kVDz0fbSrzkSE8HFlazM8I+XlQ4qW2qxxpWQj7bYpbY7EdQDt6TKlKQ7TpSxlJikmo6rJP6nE6VLFZrYnZ7H/FKYzI2GYbM5c2GKjfRUNtS7tlW6l/N2rdp0WUvFoUoKqS0sroc4FEKUStCDy95gz6o1w2HU5TlcqPK6dWs6uzpqGLDEZSqp9nR+42tPjl396YcnzszOJl/7F//dfY9+gZmwQL4nfNf1PL8MBjg45PpeEE3VOqo7N29+93A0N9sM4nCqPdVqQRGP2+3w0KFD07OXXDYTTJ46f2Hk4uXRiYmJubkG1FPm+pIlaFLfQP+KlauWLq+2dLAQp6+rV+RRHslODp4KPhmFTDkudzBnQsWqIgHJZFHTcUvdPX1lb9p3ZF//0kq9u1qe8UtuS7tjOu3mzPjY1Xa7PT0z9/Rzz39w+L1aPHX53KVzZ8bOXrwwMjGps7omQdl1/WpnvWuwo6tbsWnm8SU99a5aFEnuuOXIbEYu6eC14DxRyXFBHoIg8MslnRLB1Sl6UGDdunW/99WvvPjswcNH9k+3xqpdFa/urly7cc3q2/r6bvTo/9ORJqFJFKUWTAkJr1U7fa82tGxtz7JVS9T0ktqGqu+s3bxt2cZd+OCwoKteXb969YeHTu4/8/boLybn1OzVqakkjB1eSmITAEjHRWFBay6Kgnq9c9WajZs3r8K7JYhY2WOae/i+tnmGQTTXdIXn6PSa0jERp/S7Q7+JMERRIttK8Uqlp1rt9is+Y42KF/Z3ex6Hzq/98lutxszUFCYDazZffPHFI0fi7nI0DuZ5bvTilYnx8fF2KyrXNH8ulb1Spdo/sGztxs193SXWwDuI93dibsSSBb19XXfdeUep7L998cxf/uVfruxZ2t015HT033rnXQP95cTsHEDPpdQxcG7omP22KNrWtH8JV0rFKkbnYwbqrHsluKOCiCcx3glxEsTJnJSNVmtudCqqXBz9yfd+WK/FSTR58eLk+XNXRxtyamoqXDZU6+zfu+/W3lePT4ycm5mYvBp1nJ8IJ9vqvtVLNq0bEmwqCJtzM40kFkFb/vqVt04c+qDktCYmT5w6c/nEqanLV0ejhHm015nThnxtz8QkcBzPBOYx0V+T5sz06PRUY+f2dau3bgGRi+N4xdDAnq0bDn5w7OSZM1fHm4NDFakDZbs93UOdXT2R1BsHVi3tX7ti5dHLY5Fic3G7K2qpKBgbnxqbmvvlM89WHenMXJ2amL548eJs7ExOzzbbnZWyXqtrtlqK87UbN61ctRYzs1Krln297onfMJ+pjZtWP/H4w798/YPjxz/8+x98r1wuD3VWhtduHN52a28XSfvmnCcWFhYWn19YnnnjIQvaSGrMNFnsBBM677VifsxEM44nA39CVU/MlqaVnEviRISuq8PX6y2Wicl3kaZQh/rumCX1BFqNoze+RCwQXIe8ryqnNsvCBguuTsgTQXymR2ytuRuZu4wlHW4APTSyPPNjAP0wimVDSjk4MLz7wQce+/Kjrj+byKaMZH/fUBTpWE0uj/TKulftqHaCrlV8T5b7SpVBv1J1uMkRImSr0VbSaQdhO8ZF0vM81pZRs5lEUOYSxtvlsuN5oBDi+PHjZy/PuuWw7kflZJZ5tf6hgb7BTgxyjEkiwQR5kiRd3TUoXpHxrCaQqcLNU6Bg9GtlLpgLaK2X6Kd0WVz1fT3puBu2poPWdFJjrWYUJ6GA/ptwt1SWSbPkMDDe9w4eTo4dHewtlWbAe8V01L9m9bru/oEwSUrSZIgXfqMdt4JIgf+Cy0gZtpqxB76cRIr7oJksqlQqSsYcvRapR6SihJ0mZ8bA0NBDDz3QmnPe3X/ohRefaUatem/nlp377ry7o7NzZ1f1Bo38JwwddTNT8cH/K7PTeNgHVy3f++QffmtHPxuoNUt6w68X66cfLDGZGrn84cFjr/16/9Ejl66MjfEObQMdm0kGh9aVy1VXP9wCNKZc8kq+2901UK7U2yGrecw1N9Gxe4n2YFq5JXxWEiRTBWGj7LqUJ1Pr8+FMIgPP96VTkqLkairSLlVkyU2iAO8bL5FRELS49KJWcunsyaOjl0vurBfPdKswlpUNGzf1D/a6DkY5DhqzQdBQkpdBoUs1VK9nWZp9R29SrXR33nbXHX65dPGV4PCRI+9PvgemvXTd1kaU3HbLvnVre2nnuqHP10O+y/WzCmXSxcyn6BCu4nhZ4xUhwaITyn+s4/LGM9NN3/f7esolN56eudqO2uNzSXj60oWjr9erSZO1fb/Tdfr2bN8I3hXg4hII1poty7sPfHj+4P73mFs/fnmuc+mq1St6+v0kVA28GcIwbjXCSyCEen9mw3cbHV1ho8nWrdvc29tr7OhmRpi0SUqZ2HOMl1xP6jQlJgJd2HB1Sk882F6bS5A9vDhYvdbpY+JErldyy9VGMhMrHVs7wFMf0azGK6yd4HuYNFvtRmuuHodR2G4026fPXvjggwPdVacezZiNoOX1G7dqL+LY7AWNwiiKStVKb/+ActxQsqovWmFbeHiHuXjJ8b7OO27fMyn9Nw+dfvHF54IgrPJ4w9Zdt7f9227b1Vlj9qfLwsLC4rMGyzM/K6Bk3pLLxNh7fE94ktUDVm0zb0YlI83w4kxjpN0351diT/KSEhykAkzSxw+1JqRadQHh1LkudLI2bVLT4eR1EhTDJ2TgQF9QDjTPunRlXG4eb49cvZSccUuXumt7enrXsc4edy5mAQUi4jrkLQPTMHTFRAeSnEty9SrmafxdAri/BwlHSeCqdr3sDw6A1Xcw1kE6seuY5BDQtxyXNdpP/fKFQ2fOjUxO9SzpOPD6K7W+7m995T4tt0T5Xrnsu0oFngjLAlxAp7sbm2tEDnQ9jyl9BCqW71V3bl3/8BNf3bpzEwOlDJqiXJsrD9W6+wb7NP0QvAza1mg36ywBw6Co/qHSOQCg7Duk5mrDVYj2KFbS6RLi2IuSWurc5zbDGJOiHSkQ6HINVLSigpaMQHRL0PsYl75biqTLHb+7Xtmx77a7HnloyUCnF4z2dPVPB52hVxpa2++LpuO5zSho6iSNXmfJc3X+DeifMWTm6Yx8js+5sZrFsyF6oUPOJNpLU5vzpTHwge3IKGzMTE9MTDz++OPf+OYfQqU+cvLDF1576YWX3zhxatIXf/bgPVtu2OD/05HlMBRmB5+JbYP/k1N8tdbR7yRqeR8bGGBJw7AAGUWBXy5D2W+PjY/81Xf/RgalW26791t/9AeVXu+511797t/9YnIKOnzAzaqB45ZA5qYbzaQalzyfmfpLxjLFWTmRiRF2EmtOGMq4XeK8wrjPXFCIiLOKNiHVBXPCQJvRXUFjo5pzjSTRLrU8iWqen7hlUamVOzt2blt7x2Nf2LFtTWt6osdzWiF3+tZ4laqZhKJULnNHxDIpVcrcEOtY6kWQJI4cV0c3C2Zm2pNTy4ZX/F//p//Z8StXT158/4MTf//083/33b+dnZ4aXvEtnXjDMxkYdYiamymUKM/Du+kRd00iSR3qBu9tLsqeSCoiLisT2jt2z1+dOXD8/PRctHqof/1gn3e5Wqp29vUkX3jisVs2dG/atGI8bCSsFE6LnsFlXWvqjlLaY6Ja37Z+zZXLI8ePHB6dxdDWVqzbsnTpUrO86NTqXd3dnXgI9+3a/uATX9mwelm7OVrrSOZastqxpdbVYUzblFdTmcVMT9KsxNsjZEqPGI6USrWOkuu2Z6acoOVrr5k4mJo9c3WqqdzOzm4dAsARFd/zHNcXTlnodFtMiPFWey6QrlOqeaWejk53roS5vmbVig1btzz6xce6aqKHzclYtZy+SueSel+tGxRRlRhvO76XxLzdivDOQQv10oXv4zHQUXHjkI9NzLSiO+68+57Hfi+UpYnpqRef+cUvn3np/N98R4ZB7317uyruZ3ndwcLCwuJ3EJZn3mBAnYwT5hsFPOGyrQJHSEf40LHKDVYei71LrdLluDSmnClftT3u8lDnRtPmJ0cZ7UUvkCcmTQLgMh1CVsQm3ojOwKkcLiVXCvzF0X8pWTY0yJKr3HIjCs7JxvRk+3hzYPNAxzbf2cjaQ6VplZR8v92c1NSXsWbCOrjRSBy93SY2SkmekeV36afdZS3XUxXXDxI+lsQjc1NBtV6abWhzRBwGndWKdJTRid0jB4///LnXWh2rHvujPSvd9itvvvXar1/euWf3nhWdTiJEyQvCuagxc/KDty+dWF5f3XPmyIk3T50822zfefvezuqQp+SqFds7O3uCucsXTryzbcuyoeUr20F04PCxqfboxs2bos4lvidU4gTtpEMv5seJisJY78r1tZlbp5rTDfFC1pxFw5sTwdhEFDmlMGw405PB5Srvq5X8asvpjrxOXprC6EZhScVepxu7ciaQVeFXnDjyUWVlqGdozYqlp6+e3D+1bum+TY8rt2N0qnFm9NLEdOPBgTvKXZ4UblP7xXqcKz4363bE2lGwWovdCw4PwKtBVcqiyXizY0m36vIb0fSxD9/vHixv3LCtNa1qnrN0yBOOd/LEsad+8oulA9vvvf+xdTs2rl69uvvofuHyy5fHg+gmn2qc/glXO8snOiuR2VMWi2qzUplhV7fUg7rOZsvcaoV84SuGLWLIpqanT128XPEH/I4uUanMticvnj81OX7Jd3t9nViQtdpRvew3RFmW6q7DPR3XlCUy8kXoaqt2SQltqnJEy+niXo9XKTtXTp48+tr+tdt3qq6Bmam57Usw8twTNZTmjnR5FLRZ1euoVpbEOj1q7Dshl8zzah0rV5YGuy+dP9S8sr60aXX34Irp2da5y5fGz759z923l/yy4ZWYO2CxotWedqJ2TVNP/brwObgEiC2fnWl//0c/bbfbm/fsfPjRx9Zv33xlbLpeq5w5f2F05EoUyHKHaAdJpeTQwhab9zI2fz+btivzThT5x5TpRzrUEw9DWeWizsIpp33FnZ2eHu88cfHMT55/8833L+2+49HN/f2DuKBzZVf/qoGOs0f3v7BjzRe9Wr2n5l8dm748MXVhYvz2oVtqVce898Xqdeu3jU396BevTATO0JptTzz6zc7elZBsXQwwZ7Kzp7p8ecfc2NmrZ47tWL+ie8nKZmt2ZubS6bNv7Nmzp97b57E4bDZEHMqo1QjcRhQFLLo6frlnbknFxfTzS7VlPUvbQ4O9l48fOPhUeUPV5bWO946c+ulbxxvlwVX9/d1V/N7E7eaMjIORi6dPvvf2zjWPTs6Ov3T8zOHzVzu7BobKXZ3MVx3dyzZtcI69OHXp5OT41JrVOyry6sTIyKlTJ7zK9J337DUhkvD6UaFUrlMu82rS0ClDKy7l1VQ6XpFwPjg//atfPMMc/4FHvrB1595yyevrHxSud+Xs8bAxqRxMXva5yLBrYWFh8fmB5Zk3EtzkpwZJ1L+P0Ec80VWp1VVFTqtqxPzRxL0w41xuuhNOqelVI+3c2JRhAiIqhaOkUJTjWshicvBCDjFhHNSM/cT8WKc+uo7ZhQmqKUoJZ3ORDIScazfGR2fPis5gqTvLOrurpW42F0+XYpM23GjFadpunf1P385J8678s0vtBgID5XfoFfdgrtVoHP/wvfqLmowFcdKYm/McsWvb5l1bN7JYnf7w6Otv7ofevG3vlscfeXCdHA/mxn924tRTP31q4CuPbF7eGc0knseD9tzlS+defOa5M8M9p4+/d+DE6aVrb9u4bVPN16ETOzsGHrj/kbfefP2tN16odNR6hlY0Y3HmwiXuiM5qadWSgbrncMdrNNpTUxOJU+MiKfupG3YCPmCyazIRnj9z/NL58yOXR4+eOH95dCyK+Duv/rra3Vleu+aR+/axUufYXBBEoRJciQq4gUzmKibwcBTLpN3mSdxZX7Z6/ZbhFUeOvn/kvTdemZqaWrpxzYfnLo5MNXu7eieurF1aWwIy6larMyPTnEV132Mupqs3l2C+Nt14Lgzjss4xrxVkr9qxYv260otHX37p2anW5LEPz1ZE//aNG5YODkDhHRufHJucOvT+81dG5267fHvA2leuXOnq6lq9elt/f/+NngH/NPCcIwkddEeTMTxFnuRqKgwnGlPB3NUyawleCfH4+9q72awLxVLKelfnuk2bJ8bCD8+e+uXzz05Onz115mQUNtsNNT05AbFWyl7E2qFyZoNATE6wKNDi1lbrENW0khJq05sxeYwXzcr1a4eGDp04c/75n//i4OHTfNma7r7uVX07e9wq3itcKRk2BEvqoEjSbwRCq/0KtHMOV/vl2vING7fs3XVo/8EP3n1dxvHQ8rXHz42Mz8x4JXft6sHBrtVVD1y57Dh+s92WMvGUji6j04ZqZ2oTgUbJIIzHpqaPHvvw5PnzU1MzS5cuO39pbHJycvPmzVs2bdDJTBgrl/T7J4zCsncz/VRdEyCXx7KFxxPPF5i3DJvnjr//plu6cGr4rfffPHP6XN/w+vsffGzPpk1uEim3dtddD1y++PTpMxd+/cpLo1Mj0olGJ2anZ2T/wOA9fC/Xu+j1vtWlwyuXXxptNX8xNRms2bZ306ZNfZ3aL0D7Y0tv9bq1d951609+8d7+N18p+TqG8KUrI1dHr3R0Vlat6Fs/3MPi+PL5M2dOn5ydmj324YmRqakgDk6ePDEyOdXdUb3r1t3L8WwPDe/bt2f/u++eeu/dFypVv9Z1+sLVMxOz6zZv37ljW10bZLnLRRg0ZibGjx86+JTfHp2++M7+90dnGw/ee8eGFavwMxG5/oZt2269MPHWwZOvvPTy+MTV7tLU5NjIxcvx4JJNGzfu6lqJ7iRxjH+yNddoNZoVv+TiPRK2hO+rJBE6t3MSSPfqxNx5zJa51uTULN66Fy5cwpthaPnKpYN9EEmS/qRaWFhYWHxWcDP9eH8OYUL8aeVPOXo9V7Jai/fN8PKIcE63/YlYTERxi4VcNXkUe4EONps4Xqx/SgX53Zmk6NzEASHTonbZ0oyBFFqUoTM44CZa7RQJxSFhZiMmj7kMRagDQzbaYTjORs5M9e9a3re3c9k26KfDS2JtG41oj5/2v9WcVpjfck7M83dtATmMoN84Xml6avyV11597Z39M4224qK7sysIWl96/NG1q5a5Knr+xZdfff2Nzs7OW3dv271tTcdMde+eXa9eeOn1l5/dPtw/VN9Tgdg5uH9Jisqbb737yq8ngmCy3jPwxS89ceddt1V8vUbQM9D/9W99s6O769XXXv/e3/9QuRVR7Rhevfb+e+/bsXN7d4fOeOoK1ytVevsG/Eo1DqN2wCo60o42N7tmeWFmdPzNt9/99UsvXTx3ZXymmVR6Esm/96PvR1J1b1q/dLBUa7XLlQ4ZRw7XSmozxIxreJ4TRTH+CRW1Z6YFW7J7194ev/rC08+9/e77Tz/99PTPA6+ja/e+O+64Zd/KlcPcYVfGJiZnZzzhq3iu2Wh3lr041KE1KyVX8pYxw+vtxkyVO/qW7bmlcsfh0SMnL/78xz8aWrp6xcD6zgrfs2ug0tF1730PLVu65gffe/bA++/9+q1X5oLZzoGOnXtvu/feJ7Ztucl5ZhFZUkj9f87L5TI0Zp1bVUcAqywqW6n1DK9cf9ddd7/4/Juvv/7qK6+9tGbdEEhdR0cX6GQSNxsNVu1gQRC2Gu3erm5R7nAdFYdM+RToRXCR3oklnFc7Vm/Zftsdo6E8+Oabb8eHjrhL1956+y3tPetYZ6WFGR5FXEZBc6bVUl6ZtWOJ2dXRWW83G2ACZd/ftHFL+Wt+b0f3Cy+98p3vfCdibq176aYdOx67+9Ft27ZgwPHmaYxPc+aAL3i+43AdoUXH0Kb9mXHMk3j5qtV//Cf/4qWXXnrxxZf+8i+/EwYxXmybtt3y8KMP3X33XfW6o8NFxdJ1uK99gG8WP/3r+/e6Ovg3i/RebC6E+BA4cq5Sq1e7Stt27Lr/7kfuvXNfucSC5kSpXtp76y3V6uDf/e0PDh4++Prbb8QsHFi6Yveee2/Zu6+Kx4le3UyUevuXDa/s6utPSvHwiiUrlnSQDzrX2Zb9JRu3P1nu8TrX/+SpFzFGzBFuqbxtx9Zbbt29bt06pg3grUOHj/zy5z8HW2s0GkEQ8XLtr7/z3/B2Wz400FP9Nx27t3V19Xz9m99atmzFD//+B3//wx+MTzWWDK/dtmXbH/zJn2zeMpxI1mzOKe50YMq5/pmzZ09dPD7bHMW75e67n/i93/u9tRsGMO28cm3Pvn3dHUPSe+blA0ffOfCmE1wa6u/be+tjeNctW4r3GHMdP27KWrWOp4CrKInamPVCuDqjp8PxJnI9b+8ttziO89RTPzt8+PBLL7/KXa/e0bVu09YvPfmVW++4xXf175RrWaaFhYXFZwmWZ95wmFDstD9mtplcGFWTUXWiyk9HlYbrynLC/KYrIx5KEQkphSr5mtulJDOLWas/Kx0JJHVB4nrFW0MpnXbbRHnRm7QYGTlNeRMAkTnKraIsryipojkVTys3CK9cuDRxSFWWN4buWFLe01nz0ziZyvyHU3Y1JT6jrmufKvzKtp17/s//w/8QJiyUXPuqKl6v1ycmJqADrV+9Ep95Et5xzz3r16+v9fWv2LpLk4ZK5f4H7iuv3np6bHb3jp2dXT1i8nISBaVq1/a9t21b1yvkrOOr5avW9i3dMNidmYyhu/X03nPvfcNrNz74xdlGyGLh9Q4Mrlu7dslA3dhMdIaStes2/Omf/kuv3LVyzdaqz2IzoTD6YTsol3nnwNAd99y/bPlqzY+FF3t1zkU001CCy+765o0baq3Ozu5lAzu+sHPb5kEx92//3X8/y/s6e+urlyf/4T/8j0lt3bLVa6I4KLl81dr1j/9e1459d14eGVVlz6vVegaG1q/ZVPbBJ9nStZv+3X//7y81e5csWdndV2ZO4PYsufveBwdWbS919vV0mdSi4Jm8zsulpasGoYbeOTk7ORd4bt1RlY3rVhnmxUuVzvWbtn3rDwduOXVR6mCc7d6hniUr1iwZWumIj9DibyKkLgCCpZGdJSiYEPyrX/3KyJ1TA4NL+/s7GYXM0Y6zocd9PRscCLvvyS99Zeu2fVcuT4VRVO90dbSexG+HavnStfW6Ll8vda5fv/GP//BfONVhjAKUdB39S4euUiJ/0+uMI3XmOffe/+CqdTvHpyZjrxx39PQO9NZrNbRty46d/+P/9H8JakuWDq+sVTke877hVY9+6cvr7xD1gWX1alWxQCmxavXaLz5Z2bp918R0k/u1tnS7+wbWbVznuSyM4oonMDluvfXO/7m2qtK9ZMWypVJSBkndBM068RpTbPXGTeVabf2mTY1mOwzDWlcvpvHqNeu6uytEjH0P77zkc7CUlajE4aJW67jrrrv6epe5TqXVlK5XaietTZu2LF+yqmSej1K9Aw+uTNTmzRu/9Qe//8j0ozONmXbcrHX0LFmyfuWaYb3RmTtpnHKvsm7z1n/153+uvGrXkuHOij5aIqdavJVcr3N4zaOPDA4uW9fSIZ8FqOaKlcPLly/t7KgmiQ7HtXPnzkqpDMl7bkkIR1TrU5FqBGG15Nxx2y1lExe5d8myvbfcun7jpktXRpqtsGdgCferq1YsRSPwPJbwunNcKeWKZcu27d7Sv6ybOe2O7p6B3nWbNgxkGyuEW65t2Lbja6JjzwPN2cZMp9/yHdHds3rV2pWYMHpXp1Tlcu2BBx7YuvPurqGN/X36rem6PskNggqDlu+Xdu3b19PTc/r06blGk3hmV3f/+s2bKY6V59z87wcLCwuLzxcsz7yhAPfToVoU/jgR649k5+isc7bZHqt0ySVK6n1S+AUux7HLlOMo33EMRRRkxjQ2Ea2/JSZWoCQFVqW+Q+Y3l/Y1ScNHY0pekJj8EY5mpB6YJtimkygdrTZMSkpVWDm81J4eH2lMTPuJ27F3wAvMDd1c2csddP/55XWjofc78nJv7z0PPgQinySaZ0LH8suaFpBc8J07YvP2HWL7duaVpsNER/yNQ1DN2/ft2KWDvrBGu810FktZ7ezp7Ol/6JF7O2vMOLmakEssNUjqxQQuuvqHdg4th04ZK51JnfbJKhNpSKdgTES9s/fBBx+SOpiHcZpVxvaM0XUFhZRdvm7j8lXrmdDhPiV3MeRcb7NloW5xyLzywPLhL2xegmq90NtwSz/u0QqZW2nccee9LdapI4VS00re0Mo1Q6gK0NYmNtcKS77jpTPC27Z91ya3otugo75gApaHVq8bWLFKVGpKW8JRi4ujUB2Fw7dt27LTKZu9vrrNwgTllAkoa9Wv8o3b+jdu2860724kPC+WJnrnzWLT+hgUcplQvBhQQcdhe/fuAJ/iYp5SSeJj3MQpVU67HVTqHVu2bN661RWONo5hAHA+jqCO6ykRRbHns2qlE8q6dCoqjd0FeNy8LsIIc0ZqlpJ4YJtL16xbunYzTieCtc3tPBnH7cCtd97x4L2YaXGev7LevXz74HJZT1wMh3LRZG2aFP3LVvQNLE10VosKjrZjHeYHF7na/BgwXurtGXjkofVxHhfHjLJxgtBMqTU3W6nXlywfXrJiJc4H7bbjlrk3/16h+3uZ9zAr7g747OL6lNjRVuXEd8prQK2GNziiHIVmLdBkUcXrwWydJSu3EOUSi8SmzRu4p/dThEmAOaCDiGf7Dw2N9PXzUC098IXHcUliEti4XuZfghmid8g63UMd9y9bYdYFWTuUJlCPnjZS/6C4w+s2DK/dwOhQJGOU8z3zbjBrmVFgUoE6S1auXsLY+k06anSlWk+yWNaOftupIIyDKOro7rzltlu3714tTUgvXac0+Sw9XQMzk3vTtq1rBJ9tBd0lpr1h3ape/YypmMtEddeuvYpVQjLXMoqIK+iJ8X0/SWIZR6DKK9etNa8D/ZpstoKSL1pB4oMn3/TLERYWFhafN1ieeSNhfkQ9ZX72of71LK33r+mNgmar3ZJzUz4vl6HB6SBBei2aJ5wrKOtOpmxJwyuEMiFqtR5vTrPMVsJT/1jB513OlNEbEmki2aO4Y/R+HVfIUFFH2yibPmsNdDB/qLJ6dX3DcFd3Ndfu8gwsbqoIsM+BpeE3Q6xdvLTyp/VC6OwmJKcy9DKMIoixDM7JdQDgWK8OiJrvck2vfB36FwMlvMDU4JYrjlfiJoGkSje+atXNJyVPy9YonlDBSmUdwomlTtGAa7JN6mAqMnBMSpIkVomO7AoKwMjopxOpphPEeFh7Hj3saYY5owcKw+KYX/V8JyYOAL02UCHnlZI+H0Whp9krakpw8yTCTdBB0+VIR4zxzeyDIPSdtOJaIg9rLRXHvFsUpUjQEYmEybGiw9xwbcyihOqkRXKjUeqckiI1nScqcoSXJLhnXPE8V6TNvumn23wHhCEVxtwjdAzRPP+fpEwvOh4YpkerVMI8KVVMRCDHNXG8VALmqQXvpmlLgJJOaCOjqK2jfDlpbUpH9HFpKHBekClV+SwxkZHN7SLMSD2UzBWuUzbJE6OEl/SgCsMaTYO0H7bUWzv1LNEjGga6AgwN+KzhOSVzFzwXmNKODEq4vlRuBzF3Xd8MnHYUNxmbuIm/Wq5U0DiQB7x1EhUL1wMnSYy3BRGGIARZlRWvZPp3s60x8OJKnEjweAgd95mn4cH16gDRPxQrmUFUkrWDtnaM1S9ml9NOe1A/Fw8hRGRWJsivRP9mOEmYRDIsV2pBFPqeH7XmKp52cwjbgY8aibKbOuIowcu+VNJPm3n76zmXyESFMV5B9A5hnk6TRQFo9d7IOHD0goKjF86E05qdq3TUK2ZJKf35Mc+9XlXzyq7v4dWnvYLNZowwCX3HR1dpBUQvl2AKSkwCTQ1rfsnRgey8ODLvMQluy1Juq3gsQ4W3oEifBcGNE42uBK8X/DP5XkO99qSjnuHn0+TeqZh9vFGsUwP/8w2xhYWFhcU/BMszbySM4VEY1YG1BYtXs9IDw2xjmJyYa+6fEiMxn3CcpKJYOUnwy+wnEvqcn0BHgEqiw8hqMiFNMEZFeRCM6ihVSkZ1nhSTfTtNUagj0ipj4sRfVBFC5RSiJDXLVDpPdjWZbJ5avrV2y0ObNt1fGdrG+pbpJkathmdMdvixF8Z8CtrhfPZNC588BMghaY9JEuj4vtpyR3obK2ldxwkC0DTu+Tr9ZTuOXFfEUeiaZDBmYxrO+mXPC7hottoTYyMyausdedAFdSjOTJXUCfb04r0LfmjiCrciKYySB/UO7MvjsedpwoAaYyldrzqvYEmWhMp1FVSxdBOgvs5VhhvrEEGJBG+LpV638Byz01YrwaZTMXNK3PhssjBI/FI5itvge0InMXSYLzxDA4N26AqtNpb8lOUEiV4qcR1G18aGAgWtoKzJjXEQjTXfkLRWYRLmOMrB/bRpTnNhXKGnreSpPUZqqqE1WdBxaS7jQop5LfemhzLPKtf9NYo4I/d5DXRTc4AkEo7rl0qZrRcHAzNMMSQtDOfW+UKMFSuOA+GWcJHnlc2AMkPszeJFbF4OlHFHw2FeXWv3LN325uvVAn0iClsYF98t6fUDQ/ZkEikZU04R1OC5GXVSYIUVbU9LCWHL9Sv05sGsdColHfA4wrR2qmazpr4iYRHmrkpcR1MGzR00WdLmXLxUjCkbvdBJQGSsG+c4vOKXiF62g2aZ/EoX4ebhFGagpUjXAPUilO64Id5RrLds6vESrFKtY2YkYej42g4YhSG4v6PT0BozsHkmdP4Rx6wDlcoeHdAMUXZ1lrWfgy6W8kvUrN8SYHgOp10OoP1CZx/R99OTrky7KwTqT6R24cbouPrtzjHotIiG4ypRZTTM5NwMw9g3KXpRMde34612OD03OzE1OT09zUzrQDJzih0ECSitWQThSoHWukqGUg+zIoO+foVADloI+DF0HB1dwEwzyYIocXzQc/18BFGURHG1WtZ7Qny99oH3iWdMmkEQaNdazym5/OaZERYWFha/E7A88wYjCGSppHXqhmKtMiut98ur/fLKerO/JD+cmTk60xpribAmwpKX4Ee4FJtNlglPHdDSH+TFv66kmZOimevlMs96Cb3S89w4glqYRLwZ8zhWYbla4gPxrp1LV+6u7nqosv5WFlfMNlAeeFVtwDDh5dMdcopCZv5zSekzApXtiDVmnpJxR5TaAcwoZGEQQGkvGWIQJlLrc652CtVRTGIdr5MJ8DttxoBC71e79t5258BU9y27t5Z8yq4XaZOO1u/19kyhlUtpwvpqYq83qlEbQGi1h5jSmnuScMfTCqPRr7SarrSF2k09JZnR4ABHUU5UM3qgvlpn1dYVUAjHbIrUjUfLjT8mC9vSLwulzSmup2kCuqhttJFOxsrKLitVfMN3Y6jGMoqEX3F1sEcnkqxqlMfYhJ/SvBPECL3QvLPEzDoI9QJMoqrlqDfpKV2PjkIkjdk2a7eIWZhEYcWrkWUXyjC4PYjn54ZqZp4HBXA9iJgijnapJElI/ZTGetEA80GLk2kDILlPGzsPyoANgtSVhF6AMu8GFXEJPico66TJqKvTqEoVa61d75LVTFTbGfVsjLUyj7Hy0/hDiUbkg4DqbEiecbtQOswppZlgLLNOgmhE3HFLfilMYsdxE7BjodeiUgM2c6NAur5ZHnCMI4Bx9TZtUtzEbIll4jkiiaXjCrJpe2ksF2mWW0Bz4oUk87M8+otHc/4j17vmlU5mrMuAwnFGPhF6XUHLWRPORPed41kph83Ar1Q836dKBK0a4DFUkt68rVbsV12lSX5S9h2pAj0ndMJb1zGpUzHonp+aHrkgt2VWNkZnSDWKY4jdoYUbnrJcU0ifdFzXNFDPBNeltSPUDcYoNMnEg4w3Dzcxehxv9br1Dzz04MaNu5csXyaTNDBAbr72y7hJxTjWgOyi6XHZ0xuHi/ZeqZdU9KKY3oSgpKt98vG2UXghmdw8um14i3LPR3scais3TZUJ3c73DCW+2fKsWlhYWHzuYXnmjQR+Z92SiMxGPL2BKlYzM7OljpoadmrDPcGOzvC9qxPvTbKTc+WpuBpV3QQKvPAcn0NhlLHQfoZ6f2WQBJ7jSWP10GvkWrfXLMRsinONSSvSCgoHU4RuaAI5Qj8UpVi1oAqq8sxcdLl3Xf/dX9r+4LfcjhXM7WVNwRIRlHRsw9A0tqL9apnZI2f2ecpsG8/vFNtctCnVWKTS/WOgmLkwXEfkpFQHQST2pcdCW/w4dDW/60//zb9TUu9mJMZe1+p1RL6MpORpYyYT+dY0gscLyqz24AUhdalVaeqHdOlBZlsBoXoJmmB5402YIUlRM9A8nVOnZMIem+aDZOLyUlmvLmhnPm0P0e6zbrrripENznhgau6cb5zz09A2zNBO6RjlTy9PCI+iRgkKf2yMIUy61CbHhAFh6Xas+c6ib0YCRhc3vrSOI26STXofDZEOA3cWkkyeRoXO83fkg6V3Q7r6u+uWFAsE2TFNDlxGwmHSL6WesjqDrrFf5VWkvD01EgsKCUbEg6BtTUrm/F7bwowJzXgtcPMOIQfO2LyoCvmTuB4h80Ua70XmZ0Z1obeda79Nv1ToY9olJ4+MDXg0xNm+QfK9za/Q/NdEas3uu1BiNw/Sd3MaYdiQfZVa+lNarX3bU+si0+HGKumrNj1p/hbs+eWq9kLXgX98JzX/M5M0hobQgEKRZ0/VPEAwPZ2hWRa2QuQN1X7W+S5ievAJrufkRUX6EZV7t9161223PKwohxbNn+whzcyL2ajxdL0rPZKeldpbgtO85iZKtl5M9d00aWrRcSb1CGfkQ5yeKWm3Yz1nLMm0sLCw+KzB8swbDpnlJoGqlTAeSRG0PdUWTnVDuatvWW1FX2v/XPPo3OSllj8TVpTHZWK26yXaXiB9KA2+W4uSUPEsCi2XhgJAUREe1x5WZvscuGekA8zqO7ltGSS8HfIZ6c0s31h+/ME7995bW7aReX3M6zYxXDTnaZvYNNoypacKBfW7qdS7GwuVB8zgxoxgDmq+qf+ZRDHGw1mlajclVMx31zKzezHVnHIFqhBnmF3HIMZYyjDprNn9l19TgBQ5sSgeNX8d4wxcqFxez3gtCs2Q5E2dVmFsq5S0x1BLp5jTdUE189PpY4NKzZ+82TbpXRf8Ix6ixUMh/tH9NcW4GSa6qsA06J2Qp3tQGadPbcc8u3zBoy2zW3vZFtrsOL9OlGme3jX1duDFmfXxb4x5OvzR/fo8IHuOimyZXzuxM1+RRTMklyfPii0UafZ0yWLNsrggdc3qzHXHRC5IPnm9d0t6sUwXs7JixRHk1xtztSBMQBq1POO/146yifr1MctJ+nV03blxky9CWVhYWHzuYHnmjUXMWeQw6THe1lYvTiveQia+jIXP1VDJr5XEshJf39c6MBodn01OTzph2RF+CUqdjgjZwtWedIUJFBFrXzYdZyMxP/2cx2HS0Nt5tG9WyeFlyROlkpBFblmy8tzwGn/tzrVr99U2315dsUnHBxFmt4yv3fcisx7t6biVSnCKKkE6q8oXqhcqM78D4NdXi68vBNpOldow5tUvBQqmA/3SRqp5nSxeoG5er7bCOWMBULzIAFUWWMR8KWi0Bb0vO5pbFRYYiLIvSVpkYWMKuiNRGtfYuIivaI7hzIsmpTPSbPTKjwpdjESy6M2zQC8Vi/6bCv2jFd+bCP/wMo0sFFyswS8mn4sICSd+QRDZkWydYX7+LbqdZOn+4UVtSAVemF+LerFgbuSrI/MMc9F1/4glqusTqMWfP8v4qHZeY49l2cRWBQKZ+RTk5wtItyl/xM3Egn88Gzm1gI8tHoHrrgJ8PMcrttyMucqcYEXGhyWFP1/4wBao5sIJmL3BCotOIqvneu1YMPc+NysRFhYWFp9PWJ55I8H1+nGSr8ISeXMTlXAlBGu15mZVo1Tq7Fjn9vSxev+AWtHZ6rwqLwVTk5Ne5NTdihu6UCQiHT5SJw9TSu+DIa1Fcq36l3A+SaRKIimMwTPmbsD9pqrMDq32bntk+X1f6hjYxljVpDYEbUgEbb1zXAVuaZQHxzjjzXMszrK49b+TmFepSUMqanKa++n9krl/Z5rFNLvQXLVQA1MLtb153ihU5jSbM8yFhkl+jXFJqpzWcraAp2U1sPlKJFmrrtHkZGZuXXAXSj1iEGc9ZaZYTA6WDp1XKV9Nsi4UG8CNAmocBou+c/N8ZpEJT7BCYI8sk8Hi9n5+sLDrH4lrhTD/Vaa6/7ymngq6YDqTCy5fvLRh7O3zpQvjztPlgwVUZ17dz+bV4ln38bbqj4VgH8E1bk58TEdSGakF1sBFQyNTf1GWrfctFrfJKkM8lf7MS/4adprfZbFbw/z75x/oDRfXH25Dl80GcfVRlVB6YLNFNX3jmQRdMk3ERMtnznUvtbCwsLC4mWB55o2GJoM6KAdtfnKUTrAoFQ9LQng63Eso49nQTSqstIl1Li0NDK8cf39y8oMz8Uiz3Y7rXrUU+G7o8ZT+CeOuKHR4Wu0wGSvWdnzhOFWVqKZOtdGq98S1/vYT39y343YxvJE5fYZkuqwVhK7DPaIAnBuWKni+r29eezH4nXSg5QuomsEih9LMQXSRRSD3XXR0sFBD23IntaKGx1hqpVxIqK5hmOx6ZEPmkZ9SqqmKpSkuUO75nHnGZoprQTtNcmtk2h42v75g5mnR2kkF9CVcLaAneR7Qos2NWjxPhud74F5Xbrz46XOy+erjTNbX4pqHrDAB58+lG1iVCUwl09y5jGWLGCz/oq9Sxu9BFEYjpTcFu2VsFgh4am+XRcPpfPsLdsri+kBaYJ6//EMvio/o47Wfbzp8/FjnTrBy4aM9/0UsEA6RfKfgVysXurmynCXOP2LXkPyihTFHvsSTLRMtOHUNFlgp50vOu2FLls4xZ6FN3vw8pawyc39QqZevMitwOg6Smn+5/UNzx1o1LSwsLD7TsDzzhkK59NMrTTBHnbSay9iRieJtpmNs6NikMmlFrZi51Yorytypsc6hHr6uMnl0ZO7oZOPCVC2odLgdlcR1tBFUawl615/+5dWpsXXQfCYTdzpwAlWPh1ZXt922dOOu+p47WP8mbdCSUayTd3Pml1zt4qij+5lwDIyROsDSRWYyOckFisvvXszZ63RXXV+PpJKuVtndzFUUJC3S8VE0ZYpTJW1enlkyGgrWUtBSM7/HIvcr6PEmlGi6UXLBJTkkT0NNGSVSZZYOtYArZBXExi7KM+Pkot7RJlKfZdFGeK7qZZEwTVVuzjwUz4ySPF0KWcBG07/OQn9Ruvd1rXY3O/EokojrFmDpa3mRoYmz6/MWlVuWCtZLdq1ZrLirLSksOugrssBLuQ8nHTTxWdR8bdmfhC0kGPOV0xTVmZZSq+n/n73/DpIty+8Dv3PO9Wmrsrx9r543bV+bme6Z6cEMMIOBE4ElCDC4MoyQqGBQopa7CjG02j+k1R9SaBUrRUiglpR2g8vVrkiAAAjDxS4JYDDAuJ5p71/38768S3u9fr9zb2Zl1TP9ejrn1Xvd30/MZNfLysq8ee353uNu2xXv5t6r5bNiZ5smu57s622Y7Pw3FVkP576belkvaNWrq87fcOeE0N0Bunta/x933dYcd299+J1etvdLdBfkDvWZvYXP3k/2Nwbms2Wi77slPLdu972690e6r9l18+6eSwMAAA8x5Mx9luoigm6cSuX3uGPFoRt3OI6YRpKYUWrEXMdoqjRJm2tJulp2y8NWdc615ucL46PNdzfEFT9cN1QzdfQ0jHklBQUFThaObY77UT2xlmtT4fRj5ukvDX3x66XpU7pAqOc/V7aZFVeSKDAcMzR4eu38eq8LHbpQmZgyljsdhNROM63Pm9s6zN3zNZGQYdYaWj8f0j+FdLrlMJ5mnCux82pEc3cFxd0/V9vToFC/Y/5cIrqTJfZVZql8iJ6+uoisVizJ6z10dXrSjRndIl/vi2Yjf8he8TdvK7k3Bu9pspstp8riSlYslt3YmeRRJ09Kqi+G7SmF9/7xyIeQdKdxwP3+QdeeBsWiry9f/9q7PVfoP0zNfAvqOs++Oqtkp8Zppz6KtqVu85z2t+vOKq57TbnzPW2nZvNui3r7V+nqLujuzX1Hd1hln6xmeP/dq/3wxzUP7ub/brzPtnWWOnfO1r2oaez89u5v25/f0p2a59tW6x225p67Avn5Ie3+Uva+Uf7Urqipd6T8ZNX9qySvje8G195b9e7i7d6vd6/LPY23AQDgYYGcuZ/S7jSHvbaLsUoSQ0b8vDRS0zQlx4OU+1vJNIlVHNhRJ5VN26zOi4laQR4prL4RrL21tHWVKzYLgWVHhskXemWkKpbhVnzVGPIPni48+42jz3xdTZ0QsqITYt8oC3EkDFNYtp2mMeXenTKEzGcaSNKkrwSS9G5jf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" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;"><div style="line-height: 100%; margin-bottom: 0.21cm; margin-top: 0.21cm;">
<span style="font-size: small;"><i>Example of the "love" attack
applied to Google Perspective</i></span></div>
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</tbody></table>
<h3 align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;"> </span></h3>
<h3 align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;">Conclusions: can the deficiencies be remedied?</span></h3>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
</div>
<h3 align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;">
</span></h3>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;">Our
study </span><span style="font-style: normal;">demonstrates that
</span><span style="font-style: normal;">proposed </span><span style="font-style: normal;">solutions
</span><span style="font-style: normal;">for </span><span style="font-style: normal;">hate
speech detection are unsatisfactory </span><span style="font-style: normal;">in
two ways. Here, we consider some possibilities for alleviating the situation with respect to both.</span></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;">First, classifiers</span> are <i>too specific for particular text
domains </i><span style="font-style: normal;">and hence do not scale
across different datasets. </span><span style="font-style: normal;">The
main reason behind this problem is the lack of sufficient </span><span style="font-style: normal;"><span style="font-style: normal;">training</span> data</span><span style="font-style: normal;">.
</span><span style="font-style: normal;">In contrast, m</span><span style="font-style: normal;">odel
architecture had little to no effect on classification success</span><span style="font-style: normal;">. Alleviate this problem requires focusing further resources on the hard work of manually collecting and labeling more data. </span></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<br /></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;">Second, existing classifiers </span><i>are
vulnerable to text transformation attacks</i><span style="font-style: normal;">
that can easily be applied automatically </span><span style="font-style: normal;">in
a black box setting</span><span style="font-style: normal;">. Some of these attacks can be partially mitigated by pre-processing measures, such as automatic spell-checking. Others, however, are more difficult to detect. This is particularly true of word appending, as it is very hard to evaluate whether some word is "relevant" or simply added to the text for deceptive purposes. Like we mentioned above, avoiding the word appending attack ultimately requires re-thinking the detection process as something other than probabilistic classification.</span></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;"><br /></span></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
<span style="font-style: normal;"><span style="font-style: normal;">Character-models have a far superior resistance </span>to word boundary changes than word-models. This is as expected, since most character sequences are still retained even if word identities are destroyed. As character-models performed equally well to word-models in our comparative tests, we recommend using them to make hate speech detection more resistant to attacks.</span></div>
<div align="justify" style="line-height: 100%; margin-bottom: 0cm;">
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<style type="text/css">p { margin-bottom: 0.25cm; line-height: 120%; }a:link { }</style>Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-41571027759210376192017-10-19T22:54:00.004+03:002017-10-19T23:01:35.657+03:00Common sense applications of trusted hardware<h2>
<span style="font-size: small;"><i style="font-weight: normal;">Hardware security mechanisms like ARM TrustZone and Intel SGX have been widely available for a while and the academic research community is finally paying attention. Whether these mechanisms are safe and useful for ordinary people is hotly debated. In this post, we sketch two of our "common sense" applications of hardware-based trusted execution environments.</i></span></h2>
<i><br /></i>
Hardware security mechanisms in commodity devices like smartphones (ARM TrustZone) and PCs (TPMs, Intel SGX) have been <a href="https://doi.org/10.1109/MSP.2014.38">deployed for almost 15 years</a>! But most of this time, ordinary app developers have not had the means to actually make use of these mechanisms. Intel made their SGX SDK publicly available two years ago. This has dramatically changed the awareness and interest in hardware security mechanisms among developers and researchers. When we published the first academic research paper on TrustZone almost a decade ago, most academics didn't even recognize the phrase "trusted execution environment (TEE)". This year <a href="https://www.ndss-symposium.org/ndss2017/ndss-2017-programme/#session7">NDSS had an entire session for TEEs</a> and reportedly the most common word in the titles of the papers rejected at Usenix SEC was "SGX"!<br />
<div>
<br /></div>
<div>
Despite increased awareness, mentions of "trusted hardware" seem to elicit a general sense of mistrust among developers and researchers. One concern is that it requires having to trust the manufacturer, which is of course a reasonable concern. We often hear statements that are not exactly true, like "TEEs were designed for digital rights management (DRM)." For example, the genesis of ARM TrustZone, which dates back to the design of "Baseband 5 security architecture" in Nokia, was motivated not by DRM but by demands for technical mechanisms to comply with various requirements including <a href="https://youtu.be/PFjh-IeUJMI?t=670">regulatory requirements for type approval</a> (note: the link points to a video). Of course, once TEEs were available, people did try to use them for DRM or other applications that can be perceived as unfairly limiting the freedom of users.</div>
<div>
<br /></div>
<div>
Nevertheless, hardware security mechanisms are strong enablers that can be used to improve the security and privacy of end users. In this post, we illustrate two such <b>"common sense applications of TEEs"</b> resulting from our recent work.<br />
<br />
<h3>
Private membership test</h3>
<i><b>How can you check whether that new app you recently downloaded is actually malicious software?</b></i><br />
<br />
At first glance, it may appear that there is a well-known solution for the above question - you simply install anti-malware software (often called "anti-virus") and use it to scan all the apps on your device. In the past, this anti-malware software would periodically download a database of known malware signatures, and compare your apps against this database. Of course this approach is not always 100% accurate (e.g. the database might not contain a complete list of all malware signatures), but nevertheless it can still be used to identify known malware.<br />
<br />
However, performing this type of malware checking on a mobile device is very inefficient because every device would have to download the full malware database and expend its own energy to perform the comparisons. A much more efficient approach is to host the malware database in the cloud and allow users to upload the signatures of their apps to the cloud for checking. Statistics from <a href="https://yahooaviate.tumblr.com/image/95795838933#_=_" target="_blank">Yahoo Aviate</a> have shown that the average user installs approximately 95 apps, but according to <a href="https://file.gdatasoftware.com/web/en/documents/whitepaper/G_DATA_Mobile_Malware_Report_H1_2016_EN.pdf" target="_blank">G Data</a>, the number of unique mobile malware samples is in the millions. Clearly it is far more efficient for each user to upload a list of 95 signatures than to download a database of millions. This scheme is also advantageous for the anti-malware vendors as they can update their databases in the cloud very frequently, and they need not reveal the database in its entirety to all users. But although it is very efficient, this approach raises serious privacy concerns! It has been shown that the selection of apps installed on a user's device reveals a lot of information about the user, some of which may be privacy-sensitive.<br />
<br />
To overcome this challenge, we proposed a solution that makes use of a server-side TEE to perform a <i>private-membership test</i>. As shown in the diagram below, remote attestation is used to assure the user that she is communicating with our TEE. The user then uploads her app (or its hash) directly to the TEE, which checks if it is in the dictionary. One critical consideration in this approach is that the adversary can potentially monitor the memory access pattern of the TEE, so the TEE cannot just perform a normal search of the database. Instead we <a href="https://arxiv.org/abs/1606.01655" target="_blank">proposed a new design called a <i>carousal</i></a>, in which the entire dictionary is continuously circulated through the TEE. As we discovered through implementing this system, with the right data structures to represent the dictionary, this carousal approach can achieve surprisingly good throughput, even beating competing oblivious RAM (ORAM) schemes, because it can process batches of queries simultaneously.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgeQLv8fls5u60LzanlLBS_7vfKiTXAfwU_dtaCmEH9ktIpgNh_nRJs1gLpOThoYJd9FwRJ8Gl_9fB3Y2r1NJtm2jbqgtRSSl9OXXRzbgwy95g0uFmVm08SWD0T6FyNugFwLDLgGAM2KhuT/s1600/Carousel.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="970" data-original-width="1579" height="392" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgeQLv8fls5u60LzanlLBS_7vfKiTXAfwU_dtaCmEH9ktIpgNh_nRJs1gLpOThoYJd9FwRJ8Gl_9fB3Y2r1NJtm2jbqgtRSSl9OXXRzbgwy95g0uFmVm08SWD0T6FyNugFwLDLgGAM2KhuT/s640/Carousel.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">The Carousal approach to prevent leaking information about queries from the TEE.</td></tr>
</tbody></table>
<br />
<br />
Also, as we point out in a <a href="https://petsymposium.org/2017/papers/issue4/paper44-2017-4-source.pdf" target="_blank">recent PETS paper</a>, private membership test (and more generally, private set intersection with unequal set sizes) has many other applications beyond cloud-assisted malware checking. One example we mentioned in the PETS paper is contacts discovery in a (mobile) messaging service: when a user installs a messaging client, it checks to see whether any contacts on the user's address book are already using the messaging service. As it happens, <a href="https://signal.org/" target="_blank">Signal </a>recently <a href="https://signal.org/blog/private-contact-discovery/">announced their implementation of a private contacts discovery mechanism using SGX</a> which essentially follows the design we described in our work. It is always gratifying when real world systems deploy innovations from research!<br />
<br />
<h3>
Protecting password-based authentication systems</h3>
<div>
<i><b>How can you be sure your passwords are kept safe, even when sent to a compromised server?</b></i><br />
<div>
<i><b><br /></b></i></div>
Passwords are undoubtedly the <a href="https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-817.pdf" target="_blank">most dominant</a> user authentication mechanism on the web today. This is unlikely to change in the foreseeable future because password-based authentication is inexpensive to deploy; simple to use; well understood; compatible with virtually all devices; and does not require users to have any additional hardware or software.<br />
<br />
However, password-based authentication faces various security concerns, including <a href="https://thehackernews.com/2017/10/apple-id-password-hacking.html" target="_blank">phishing</a>, <a href="https://gizmodo.com/over-560-million-passwords-discovered-by-security-resea-1795254560" target="_blank">theft of password databases</a>, and <a href="https://www.computerworld.com/article/3086849/security/hackers-sold-access-to-170000-compromised-servers-many-in-the-us.html" target="_blank">server compromise</a>. For example, the adversary could attempt to trick users into entering their passwords into a phishing site, and then use these to impersonate the users. Alternatively, the adversary could steal a database of hashed passwords from a web server and use this to guess users' passwords. Most worryingly, a stealthy adversary might even compromise an honest server and siphon off passwords in transit as users login. Users' tendency to re-use passwords across different services further exacerbates these concerns.<br />
<br />
Current approaches for protecting passwords are not fully satisfactory for various reasons: they typically address only one of the above challenges; they do not provide users with any assurance that they are in use; and they face deployability challenges in terms of cost and/or ease-of-use for service providers and end users alike.<br />
<br />
To address these challenge, we have developed <a href="https://ssg.aalto.fi/projects/safekeeper" target="_blank">SafeKeeper</a>, a comprehensive approach to protect the confidentiality of passwords on the web. Unlike previous approaches, SafeKeeper protects against very strong adversaries, including so-called <i>rogue servers</i> (i.e., either honest servers that have been compromised, or actively malicious servers), as well as sophisticated external phishers.<br />
<br />
At its core, SafeKeeper uses a server-side TEE to perform a keyed one-way function on users' password, using a key that is only available to the TEE. This immediately prevents offline password guessing, and thus eliminates the threat of password database theft. Furthermore, using remote attestation, users can establish a secure channel directly from their browsers to the TEE, and receive strong assurance that they are indeed communicating with a legitimate SafeKeeper TEE. When used correctly, these powerful capabilities protect the user against password phishing and ensure that even if the server is compromised (or actively malicious), their passwords will be kept safe.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQZXS6wxaG-8iRBm8tH7yBqxzhYzZa_WeWthP86a0lak9WHzYo28UdzzO4UbuPII1E_QzUaSLOl1hl9FsxgemFkGX4B7peq8DI-npwZmfvOvzjcjCBWLg_y-3VAzoVGaog20zoOO-UFtGo/s1600/SystemModel.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" data-original-height="593" data-original-width="1536" height="243" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiQZXS6wxaG-8iRBm8tH7yBqxzhYzZa_WeWthP86a0lak9WHzYo28UdzzO4UbuPII1E_QzUaSLOl1hl9FsxgemFkGX4B7peq8DI-npwZmfvOvzjcjCBWLg_y-3VAzoVGaog20zoOO-UFtGo/s640/SystemModel.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">SafeKeeper: Protecting passwords even against an actively malicious web server.</td></tr>
</tbody></table>
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<br />
We have implemented SafeKeeper's server-side password protection service using <a href="https://software.intel.com/en-us/sgx" target="_blank">Intel SGX</a> and integrated this with the <a href="http://www.openwall.com/phpass/" target="_blank">PHPass</a> password processing framework, which is used in multiple popular platforms, including WordPress and Joomla. We have developed the client-side component as a Google Chrome plugin. Our 86-participant user-study confirms that users can use this tool to protect their passwords on the web.<br />
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<br />
<h3>
Epilogue: Enabling more common-sense applications with Intel SGX</h3>
In the process of designing and implementing these applications, we encountered a couple of challenges to be solved before the applications can be deployed at scale. The first is that SGX enclaves can only run in "<a href="https://software.intel.com/en-us/blogs/2016/01/07/intel-sgx-debug-production-prelease-whats-the-difference" target="_blank">release mode</a>" (i.e., with full memory encryption etc.) if they have been signed by an authorized key, which requires a <a href="https://software.intel.com/en-us/sgx/commercial-use-license-request" target="_blank">commercial use license</a> from Intel. However, we expect that this would not be a challenge for a company to obtain. The second challenge is that we would like anyone to be able to verify the remote attestation of our enclaves. At the moment, verifying SGX attestation requires users to <a href="https://software.intel.com/en-us/articles/certificate-requirements-for-intel-attestation-services" target="_blank">register their public keys</a> with the Intel Attestation Service (IAS). It is probably not realistic to expect all our end users to do this, so for SafeKeeper we implemented a proxy that accepts attestation quotes from clients, sends them to IAS for verification (using our registered key), and returns the results to the client. Fortunately, IAS signs the verification result with a published Intel key, so clients can verify the response <i>without having to trust our proxy</i>. We are still investigating whether this is the optimal solution going forward.<br />
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- <a href="https://ajpaverd.org/" target="_blank">Andrew Paverd</a> and <a href="http://asokan.org/asokan/">N. Asokan</a></div>
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<i>Disclaimer</i>: By way of full disclosure, the authors are part of the <a href="http://www.icri-sc.org/">Intel Collaborative Research Institute for Secure Computing</a> which receives funding from Intel. But the views in this article reflect the opinion of the authors.</div>
N. Asokanhttp://www.blogger.com/profile/08388956492499187497noreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-9578333644579136752017-09-12T12:00:00.002+03:002017-10-13T08:48:12.508+03:00Leading European cybersecurity research organizations and Intel Labs join forces to conduct research on Collaborative Autonomous & Resilient Systems<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
<span style="font-family: "arial"; font-size: 11pt; white-space: pre-wrap;"><i>The new research institute operates in the fields of drones, self-driving vehicles, and collaborative systems in industrial automation.</i></span></div>
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<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Intel and leading European universities in the field of cybersecurity and privacy will open a new initiative for “Collaborative Autonomous & Resilient Systems” (CARS) to tackle security, privacy, and functional safety challenges of autonomous systems that may collaborate with each other – examples are drones, self-driving vehicles, or industrial automation systems.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">The goal of the CARS lab is to kick off longer-term collaboration between leading research organizations to enhance resilience and trustworthiness of these cutting-edge technologies. Collaborating institutions include TU Darmstadt (Germany), Aalto University (Finland), Ruhr-Universität Bochum (Germany), TU Wien (Austria), and Luxembourg University (Luxembourg). The coordinator of the new CARS lab will be TU Darmstadt serving as the hub university, where several researchers of Intel Labs are located. The research teams will collaborate with Intel teams on campus to design, prototype, and publish novel and innovative resilient and secure autonomous collaborative systems and to build an ecosystem to further validate their ideas in real-world scenarios.</span></div>
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<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 700; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Successful collaboration continues</span></div>
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<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 700; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><br /></span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">The CARS lab is a new Intel Collaborative Lab as a continuation of the extremely successful Intel Collaborative Research Institute for Secure Computing (ICRI-SC) that included TU Darmstadt and Aalto University and focused on mobile and IoT security between 2012 and 2017. Noteworthy achievements resulting from this collaboration include Off-the-Hook, a client-side anti-phishing technique and SafeKeeper which uses Intel Software Guard Extensions to protect user passwords in web services; as well as TrustLite, a lightweight security architecture for IoT devices.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">In the renewed collaboration Intel and the – now five – partner universities focus on the study of security, privacy and functional safety of autonomous systems that collaborate with each other.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">TU Darmstadt aims to build on the foundation of the promising results from the initial work in the Intel institute and will address more complex systems with advanced real-life collaboration and attack models and defenses.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">“We have already had five years of very successful and fruitful experience with the collaborative research lab between TU Darmstadt and Intel. Since 2012 our researchers could closely work with an Intel team located at TU Darmstadt leading to highly impactful results in the area of embedded and IoT security. I am confident that the new and much larger CARS lab that integrates top researchers from five European universities is an excellent foundation for a creative, dynamic and highly innovative environment, and will strongly contribute to the new theme of security and safety for autonomous and intelligent systems”, adds Professor Ahmad-Reza Sadeghi from TU Darmstadt.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> </span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Aalto University plans to address autonomous systems security by focusing on security and privacy of machine learning and distributed consensus.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">“Over the past four years, Intel has invested significantly to support research in our Secure Systems group. Regular interactions with Intel engineers and leaders allow us to gain valuable insights into real-world security and privacy challenges and steer our research accordingly. Being able to identify and work on real problems is hugely motivating for my students and me personally. I am delighted that Intel has decided to extend their collaboration with us in the renewed lab”, explains Professor N. Asokan from Aalto University.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> </span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Ruhr-University Bochum (RUB) will advance the security of autonomous platforms and the self-defense capabilities of distributed systems by reconfigurable extensions to protect such systems from advanced attacks.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">"We have worked on cyber security in CARS since 2003, and were one of the first groups internationally in this area. We are excited to collaborate with other leading academic institutions and Intel in the emerging area of security for autonomous systems. In particular, we hope that our expertise in hardware and system security will become a valuable part of the collaboration", says Professor Christof Paar from RUB.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> </span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Luxembourg University leverages intrusion tolerance and self-healing paradigms, to achieve automatic and long-lived resilience of CARS systems to both faults and hacker attacks.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">“As a recently established research group in Luxembourg, we are proud Intel has entrusted our team CritiX at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) to face the resilience challenges of future collaborative autonomous system. I anxiously look forward to start working with my colleagues in CARS’ inspiring research environment, with a world leading hardware manufacturer on our side”, says Professor Paulo Esteves-Veríssimo from Luxembourg University.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> </span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">TU Wien will contribute with its experience in designing fault-tolerant systems; specifically with non-conventional hardware architectures that are both energy efficient and robust against a large spectrum of faults and attacks.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> “TU Wien is now joining the team, and we are thrilled about the opportunity to contribute to this unique research concept. Making systems safe and secure at the same time is definitely a great challenge, and having experts covering different perspectives of that challenge in the team is a first-class prerequisite for coming up with substantially new and sustainable solutions. At the same time the close collaboration with Intel will guide our focus to those aspects that are most relevant for large-scale industrial applications of the future. This is an ideal setting for influential research”, says Professor Andreas Steininger from TU Wien.</span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> </span></div>
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 700; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">More information</span></div>
<br />
<div dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;">
<span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Please visit the website of the new CARS lab at the ICRI-SC:</span><a href="http://www.icri-sc.org/" style="text-decoration: none;"><span style="background-color: transparent; color: black; font-family: "arial"; font-size: 11pt; font-style: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> </span><span style="background-color: transparent; color: #1155cc; font-family: "arial"; font-size: 11pt; font-style: normal; font-weight: 400; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap;">www.icri-sc.org</span></a></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-63822357608723807412017-09-04T20:21:00.000+03:002017-10-18T08:47:23.844+03:00Voiko älykodissa elää turvassa? Entä mitä riskejä liittyy fiksuun sähköverkkoon tai kulkuneuvoihin? <b>Puettava laite voi tehdä sinusta haavoittuvan – älykkäiden laitteiden tietoturvaa puidaan Aalto-yliopiston asiantuntijaseminaarissa 7. syyskuuta</b><br />
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Tervetuloa kuulemaan tietoturvan huippuasiantuntijoiden esityksiä Aalto-yliopistoon. Seminaarin aluksi julkaistaan uusi Intel Labsin yhteistyöaloite, jossa Aalto-yliopistolla on merkittävä rooli useiden muiden kansainvälisten yliopistojen rinnalla.<br />
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Aika: Torstaina 7. syyskuuta klo 9.00–16.30
Paikka: Tietotekniikan talo, luentosali T1, Konemiehentie 1, Espoo.<br />
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Seminaarissa puhuvat tietoturva-alan kiinnostavimmat asiantuntijat. Suomalaispuhujina esiintyvät <b>Pekka Sivonen</b> Tekesiltä, <b>Sasu Tarkoma</b> Helsingin yliopistosta ja <b>Jari Arkko</b> Ericssonilta. Ulkomaisia vieraita ovat <b>Ahmad-Reza Sadeghi</b>, TU Darmstadt, <b>William Enck</b>, NC State University, <b>Gene Tsudik</b>, University of California, Irvine, <b>Patrick Traynor</b>, University of Florida, ja <b>Mihalis Maniatakos</b>, New York University Abu Dhabi. Englanninkielistä seminaaria isännöi <b>N. Asokan</b> Aalto-yliopistosta.<br />
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Seminaarin ohjelmassa muun muassa:
Ahmad-Reza Sadeghi TU Darmstadtista avaa esineiden internetiin liittyvien laitteiden haavoittuvuutta. Tietoturva- ja hakkerointiriski voi koskea paitsi älykästä sähköverkkoa, älykkäitä kulkuneuvoja ja koteja, myös henkilökohtaisia, puettavia älylaitteita. Tutkimuksissa näissä laitteissa on havaittu useita haavoittuvuuksia, ja joissain tilanteissa voi olla järkevää teknisesti eristää haavoittuvat laitteet turvallisista. Sadeghi kertoo puheenvuorossaan tarkemmin laitteiden automaattiseen tunnistamiseen ja eristämiseen liittyvistä mahdollisuuksista.<br />
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Terrence O' Connor (William Enckin sijasta) North Carolina State Universitysta käsittelee älykotien haasteita ja esittää mahdollisuuksia niiden tietoturvan parantamiseksi. Jokainen uusi kotiverkkoon kytketty laite uhkaa kotiverkon tietoturvaa – miten uhka voidaan ratkaista? Huomio, huonoista sääolosuhteista johtuen, William Enck joutui perumaan tulonsa ja hänen puheensa pitää Terrence O'Connor.<br />
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Jari Arkko Ericssonin tutkimusyksiköstä puhuu siitä, millaisia esineiden internetiin liitettävien laitteiden kannattaisi olla – ja mitä riskejä niihin liittyy. Globaalissa mittakaavassa olennaista on, että laitteet ovat avoimia ja yhteensopivia, mikä mahdollistaa riippumattomuuden yksittäisestä laitevalmistajasta, käytettävien pilvipalvelujen vaihtamisen ja laitteiden ohjelmistojen päivittämisen. Riskeistä suurin osa liittyy tietoturvakäytäntöjen puutteisiin, esimerkiksi valmistajien käyttämiin vakiosalasanoihin.<br />
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Seminaarin järjestää SEcuring Lifecycle of Internet of Things (SELIoT) -hanke, jonka tavoitteena on turvata älykkään laitteen koko elinkaari. Hankkeessa ovat mukana Aalto-yliopisto sekä University of Florida ja University of California Irvine ja sitä rahoittavat Suomen Akatemia ja yhdysvaltalainen National Science Foundation (NSF).<br />
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Seminaarin ohjelma: <a href="http://www.aalto.fi/en/current/events/2017-07-12/">http://www.aalto.fi/en/current/events/2017-07-12/</a></div>
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Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-77055389297025887512017-07-02T22:36:00.001+03:002017-10-14T08:56:11.915+03:00Erasmus Mundus Program on Security and Cloud Computing (SECCLO)<div class="MsoNormal">
<span lang="EN-US" style="font-family: "arial" , sans-serif; mso-ansi-language: EN-US;">Many of the current members of the <a href="https://wiki.aalto.fi/display/sesy/Secure+Systems">Secure Systems Group</a>
and <a href="https://haic.aalto.fi/">HAIC</a> have their background in <a href="http://nordsecmob.aalto.fi/en/">NordSecMob</a>,
the long-running Erasmus Mundus Master’s program that is coming to a close. This
program has brought exceptional information-security students to Aalto and the
partner universities. It has enabled our partner companies to recruit thesis
students and graduates otherwise would be difficult to come by in such numbers.
For us teachers and researchers, the diverse backgrounds and experiences of the
international students have made a huge difference in the classroom and in research projects. <o:p></o:p></span></div>
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<span lang="EN-US" style="font-family: "arial" , sans-serif; mso-ansi-language: EN-US;">Therefore, I’m extremely happy to announce that the NordSecMob
consortium will continue in the form of a new joint MSc program. We have received
<a href="https://eacea.ec.europa.eu/sites/eacea-site/files/2017_emjmd38-selection_results.pdf">European
Commission funding</a> for an <b>Erasmus
Mundus Joint Masters Degree Program</b>, called <b>Security and Cloud Computing (SECCLO)</b>. With a total budget of
almost 3M€, the new program will last until 2022, or three intakes of 20+
students each. In addition to being able to offer
scholarships to excellent students, we will continue to be listed in the
Erasmus Mundus catalogue, where many potential students start their search for
MSc programs.<o:p></o:p></span></div>
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<span lang="EN-US" style="font-family: "arial" , sans-serif; mso-ansi-language: EN-US;">Each student in the new program will spend their first
year at Aalto learning fundamental knowledge of information security as well as
cloud and mobile computing. After a summer internship in industry, they will continue
to one of our partner universities (<a href="http://www.dtu.dk/english">DTU</a>, <a href="http://www.eurecom.fr/en">EURECOM</a>, <a href="https://www.kth.se/en">KTH</a>, <a href="http://www.ntnu.edu/">NTNU</a>, or <a href="https://www.ut.ee/en">Tartu</a>), each offering
their own specialization such as cloud or network security or cryptography. EURECOM
in France is a new addition to the consortium. In the last six months of the
two-year curriculum, the students will be doing their thesis in industry or
with a university research group. Each student will get a Master’s degree from both
Aalto and a partner university. <o:p></o:p></span></div>
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<span lang="EN-US" style="font-family: "arial" , sans-serif; mso-ansi-language: EN-US;">Aalto students might note that the Erasmus Mundus
program has the same name, <a href="http://studyguides.aalto.fi/2017-ccis/majors/security-and-cloud-computing.html">Security and Cloud Computing, as an existing majorin our MSc program</a>. Indeed, the only difference is that Erasmus Mundus students leave for a partner university in the second year. The curricula were planned together (as an update of the NordSecMob program and the earlier Security and Mobile Computing major) and the first years are identical. We even plan to offer</span><span style="font-family: "arial" , sans-serif;"> the same exchange opportunities in the partner universities to all information-security students. If
you are a current MSc or BSc student at Aalto focusing on security, please
contact <a href="https://people.aalto.fi/new/tuomas.aura">Tuomas Aura</a> or <a href="http://asokan.org/asokan/">N. Asokan</a> to learn more.</span></div>
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<span lang="EN-US" style="font-family: "arial" , sans-serif; mso-ansi-language: EN-US;">As a result of the SECCLO funding, we can expect more outstanding
MSc students to Aalto every year for majoring in information security. An
important factor in the program proposal was the support and involvement of the
parter companies, Nokia Bell Labs, Cybernetica, F-Secure, Guardtime, Intel and
VTT, as well as the <a href="https://haic.aalto.fi/">HAIC</a> initiative and industry
funded scholarships that enable long-term sustainability of the program. We
invite new companies with an interest in security education and our graduates to
join HAIC to ensure the long-term success of the program. Naturally, all the
SECCLO students will be part of HAIC. They will seek summer internships and
thesis positions during their study program. In other words, SECCLO will
greatly amplify our ability to meet the goals behind the setting up of HAIC.</span><br />
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Tuomas Aurahttp://www.blogger.com/profile/07721772661087693396noreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-51386551674444364832017-03-12T14:56:00.000+02:002017-10-14T08:58:46.019+03:00Ethics in information security<div class="MsoNormal">
<span style="font-size: 12.0pt; line-height: 107%;">Our
societies are undergoing pervasive digitalization. It is not an understatement
to say that every facet of human endeavor is being profoundly changed by the
use of computing and digital technologies. Naturally such sweeping changes also
bring forth ethical issues that computing professionals have to face and deal
with. The question is: are they being equipped to deal with such issues?<o:p></o:p></span></div>
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<span style="font-size: 12.0pt; line-height: 107%;">Ethical concerns
in computing are widely recognized. For example, the recent upsurge in the
popularity of applying machine learning techniques to a variety of problems has
led to several ethical questions. Biases inherent in training data can render
systems based on machine learning to be unfair in their decisions. Identifying
such sources of unfairness and making machine learning systems accountable is
an active research topic. Similarly, the rise of autonomous systems has led to
questions like how to deal with the moral aspects of autonomous decision making
and how societies can respond to people whose professions may be rendered
obsolete by the deployment of autonomous systems.<o:p></o:p></span></div>
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<span style="font-size: 12.0pt; line-height: 107%;"><b>Ethics in information security: </b>The
profession of information security has its own share of ethical considerations
it has been grappling with. Among them are: privacy concerns of large scale
data collection, the use of end-to-end cryptography in communication systems, wiretapping
and large scale surveillance, and the practice of weaponizing software
vulnerabilities for the purpose of “offensive security”.<o:p></o:p></span></div>
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<span style="font-size: 12.0pt; line-height: 107%;"><b>The Vault 7 story: </b>The latter
issue was brought forth in dramatic fashion earlier this month when Wikileaks
published a collection of documents which they called <a href="https://wikileaks.org/ciav7p1/">“Vault 7”</a>. It
consisted of information on a very large number of vulnerabilities in popular
software platforms like Android and iOS that can be used to compromise end
systems based on those platforms. That national intelligence agencies use such
vulnerabilities are offensive weapons did not come as a surprise. But the Wikileaks
revelation led to a flurry of discussion on the ethics of how vulnerabilities
should be handled. Over the years, the information security community has
developed best practices for dealing with vulnerabilities. Timely and “responsible
disclosure” of vulnerabilities to affected vendors is a cornerstone of such
practices. Using vulnerabilities for offence is at odds with
responsible disclosure. As <a href="https://conspicuouschatter.wordpress.com/2017/03/08/what-the-cia-hack-and-leak-teaches-us-about-the-bankruptcy-of-current-cyber-doctrines/">George Danezis, a well-known information security expert and professor at University College London, put it, “Not only government “Cyber” doctrine corrupts directly this practice, by hoarding security bugs and feeding an industry that does not contribute to collective computer security, but it also corrupts the process indirectly.”</a> But when a
government intelligence agency finds a new vulnerability, the decision on when
to disclose it to the vendors concerned is a complex one. As <a href="http://www.crypto.com/blog/between_immediately_and_never/">another well-known expert and academic, Matt Blaze pointed out</a>, on the one hand, an adversary
may find the same vulnerability and use it against innocent people and
institutions, which calls for immediate disclosure leading to a timely fix. On
the other hand, the vulnerability can help intelligence agencies to thwart
adversaries from harming innocent people which is the rationale for delaying disclosure. <a href="http://www.crypto.com/blog/between_immediately_and_never/">Blaze reasoned</a> that this decision should be
informed by the likelihood that a vulnerability is rediscovered but concluded
that despite several studies, there is insufficient understanding of factors that
affect how frequently a vulnerability is likely to be rediscovered.<o:p></o:p></span></div>
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<span style="font-size: 12.0pt; line-height: 107%;"><b>Equipping infosec professionals: </b>That brings
us back to our original question: are information security professionals have
the right knowledge, tools and practices for making judgement calls when
confronted with such complex ethical issues. Guidelines for computing ethics have
existed for decades. For example <a href="https://www.computer.org/cms/Publications/code-of-ethics.pdf">IEEE Computer Society and ACM published a code of ethics for software engineers</a> back in 1999. But to what extent do such
codes reach practitioners and inform their work? There are certainly efforts in
this direction. For example, program committees of top information security
conferences routinely look for a discussion on “ethical considerations” in
submitted research papers that deal with privacy-sensitive data or vulnerabilities
in deployed products. They frequently grapple with the issues involved in
requiring authors to reveal datasets in the interests of promoting
reproducibility of research results while balancing considerations of people
from whom the data was collected. But this needs to be done more systematically
at all levels of the profession.<o:p></o:p></span></div>
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<span style="font-size: 12.0pt; line-height: 107%;">Ethical
considerations in information security cannot be simply outsourced to
philosophers and ethicists alone because such considerations will inevitably
inform the very nature of the work done by information security professionals.
For example, several researchers are developing techniques that allow
privacy-preserving training and prediction mechanisms for systems based on
machine learning. Similarly, <a href="http://www.crypto.com/blog/between_immediately_and_never/">as Matt Blaze pointed out</a>, active research is
needed to understand the dynamics of vulnerability rediscovery.<o:p></o:p></span></div>
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<span style="font-size: 12.0pt; line-height: 107%;">Should undergraduate
computer science curricula need mandatory exposure to ethics in computing?
Should computer science departments host computing ethicists among their ranks?<o:p></o:p></span></div>
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<span style="font-size: 12.0pt; line-height: 107%;"><b>Silver lining: </b>Coming back
to the Vault 7 episode, there was indeed a silver lining. The focus on amassing
weaponized vulnerabilities to attack end systems suggests that the increasing
adoption of end-to-end encryption by a wide variety of messaging applications
has indeed been successful! Passive wiretapping is likely to be much less
effective today than it was only a few years ago.<o:p></o:p></span></div>
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N. Asokanhttp://www.blogger.com/profile/08388956492499187497noreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-62365060389460882052016-06-28T04:02:00.000+03:002017-10-18T08:47:49.457+03:00Off the hook: A New Privacy-Friendly Phishing Protection Add-on<span style="font-size: small;"><i>Phishing attacks are those in which unsuspecting users are lured to a rogue website that mimics a legitimate website and are fooled into giving up their login credentials or other sensitive information to the rogue site. It has been a serious threat against ordinary users on the Internet. Current solutions for steering users away from phishing websites are typically server-based and have several drawbacks. They impact user privacy and are not effective against attackers who can adaptively serve different content to different clients. Moreover, while they might warn a user about a phishing website, they offer no guidance as to how the user can find the real website that they intended to go to in the first place.<br />To address these limitations, we have developed <span style="font-family: "courier new" , "courier" , monospace;"><b>Off the hook</b>,</span> a phishing website protection add-on. <span style="font-family: "courier new" , "courier" , monospace;">Off the hook</span> is implemented entirely on client-side. Consequently, it is privacy-preserving and is able to detect adaptive attacks. It is very efficient and fast at detecting phishing websites. It can also identify the target of a phishing website . The add-on displays warning messages in real-time as a user is browsing the Web. </i></span><br />
<br />
<b>Challenges in Phishing Detection:</b> Phishing websites do not exploit any technical flaw but take advantage of the inability of ordinary users to effectively link a physical identity (e.g. a person, a company) to an electronic identity (e.g. a website, an email). Phishers mostly use social engineering techniques to impersonate an identity to lure their victims. Given their non-technical nature, protecting against phishing attacks is challenging. In addition, phishers use tricks such as serving different content (legitimate/phishing) to different client requests in order to defeat current centralized detection systems that crawl the Web from a single location. Blacklists of URLs are built based on this centralized analysis; when a user visits a webpage, it is checked against these lists to warn the user about phishing websites. This requires users to effectively disclose their entire browsing history to the centralized anti-phishing services, thus raising a serious privacy concern.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><img border="0" height="337" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEidjLbnLQLO9O5M5IY68i7EsoNP2xQZwEkvvcpj3M7O4uS9V5nUmqS_mz3wkjPcALhNRf1aVyMdzy8c3ydonrRJz0fkKftoQ7rCDGKduPELQgKfN1LzPtlzmW8KiO6o3-Hc-ZOFBImfQEB5/s320/Untitled.png" style="margin-left: auto; margin-right: auto;" width="560" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;">An example phishing page</td></tr>
</tbody></table>
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEidjLbnLQLO9O5M5IY68i7EsoNP2xQZwEkvvcpj3M7O4uS9V5nUmqS_mz3wkjPcALhNRf1aVyMdzy8c3ydonrRJz0fkKftoQ7rCDGKduPELQgKfN1LzPtlzmW8KiO6o3-Hc-ZOFBImfQEB5/s1600/Untitled.png" imageanchor="1"></a><br />
<b>Control and Constraints:</b> A phisher mimics the look and feel of a target website. However, he does not have complete freedom in doing so. Some attributes of a webpage (displayed in a browser) are <b>beyond the control</b> of the phisher while others <b>impose certain constraints </b>on him. We modeled these aspects of control and constraints in the features we chose for <span style="font-family: "courier new" , "courier" , monospace;">Off the hook</span>, our machine learning based detection system. By assessing the consistency of the information contained in controlled/uncontrolled and constrained/unconstrained elements of a webpage, <span style="font-family: "courier new" , "courier" , monospace;">Off the hook</span> is able to identify phishing websites and their targets effectively. <span style="font-family: "courier new" , "courier" , monospace;">Off the hook</span> outperforms several state-of-the-art methods in terms of accuracy and speed while distinguishing itself in its privacy-friendliness and its resilience to adaptive attacks. In addition, the identification of phishing webpage relies solely on the data extracted from the webpage and no external source of information which makes it language- and brand-independent and admit a client-side-only implementation.<br />
<br />
<b>Add-on: </b><span style="font-family: "courier new" , "courier" , monospace;">Off the hook</span> is implemented as a browser add-on that analyses visited webpages in real-time. If a webpage is identified as phishing, interaction with the website is disabled and an interstitial warning message pops up in less than half a second. The user is then presented with several choices including the option of being redirected to the identified likely target, getting back to a search engine or proceeding to the website with the option to whitelist it. The user interface has been refined using a cognitive walkthrough exercise in order to provide intelligible and efficient warnings to protect unsavvy users.<br />
<br />
<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><img border="0" height="328" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgj5yHcvFKCmRQQrRNYlEMX_ZJga7BtHEjyrvPWgRWmdjbLOUXoIDB21RDthzL1DIEwyc2UeNCNgPfvotp_kaYTHzUis_QJ7uEn9KqPuHEB-bgvHR5Ty8GlZXc-re2LWlTCSsApiaPPVlPW/s320/hookwarning.png" style="margin-left: auto; margin-right: auto;" width="560" /></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Interstitial warning page from <span style="font-family: "courier new" , "courier" , monospace;">Off the hook</span></td></tr>
</tbody></table>
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<span style="font-family: "courier new" , "courier" , monospace;">Off the hook </span>add-on is available for Mozilla Firefox and Google Chrome, and supported on Windows, Mac OS X and Ubuntu. Download and installation instructions are available in <a href="https://ssg.aalto.fi/projects/phishing/" target="_blank">our project page</a>.<br />
<br />
More detailed information about the phishing detection and target identification methodology can be found in our <a href="http://www-higashi.ist.osaka-u.ac.jp/icdcs2016/program.htm#session5A" target="_blank">ICDCS paper</a> and our <a href="http://arxiv.org/abs/1510.06501" target="_blank">technical report</a>. Implementation details for the add-on and phishing warnings are available in our <a href="http://www-higashi.ist.osaka-u.ac.jp/icdcs2016/posters.htm" target="_blank">ICDCS demo paper</a>.<br />
<br />
This work is supported by the <a href="http://www.aka.fi/en">Academy of Finland</a> (via the Contextual Security project) and the <a href="http://www.icri-sc.org/">Intel Collaborative Research Institute for Secure Computing</a>.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-80959815206666270122016-05-26T22:49:00.000+03:002017-10-13T08:51:45.846+03:00HAICToday, the Heads of CS departments at Aalto University and University of Helsinki made a joint announcement establishing <a href="http://haic.aalto.fi/">Helsinki-Aalto Center for Information Security HAIC</a>. HAIC is intended to serve as a main focal point for research and education in the broad areas of information security & privacy at both universities. Initially HAIC will focus on selecting top incoming MSc students choosing to specialize in information security in our MSc programs.<br />
<br />
This is an important and timely milestone. The <a href="http://nordsecmob.aalto.fi/en/)">NordSecMob </a>joint degree program has been a resounding success during the past decade in training top-notch students from Finland and abroad at Aalto University. Many of them have gone on to work in Finnish industry. Some have continued with doctoral studies both a Aalto University and at other top universities in Europe. One of the reasons for NordSecMob's success is its scholarship program. But <a href="http://nordsecmob.aalto.fi/en/)">NordSecMob </a>is coming to an end. At the same time, Finnish universities <a href="http://www.studyinfinland.fi/tuition_and_scholarships/tuition_fees">will introduce tuition fees for students from outside the EU/EEA area</a>. This is what makes HAIC timely: both universities have allocated some scholarships for MSc students; this will help us attract and train similar levels of top quality students in information security.<br />
<br />
Over time, we plan to extend HAIC in several ways. The first and most important among them is to reach out to our industrial partners in Finland and inviting them to sponsor more scholarships under the auspices of HAIC. The quality and international recognition of infosec research at Aalto University and University of Helsinki have significantly risen in the past few years. But the trend in government funding for higher education and research Finland is unmistakably downwards. Finnish industry stepping up its support is therefore crucial for ensuring our research and education continues to excel and expand. I am looking forward to dialogue with industry partners.Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-82648813036108727882016-02-22T11:09:00.001+02:002017-10-18T08:48:17.812+03:00OmniShare: Encrypted Cloud Storage for all your Devices<i><span style="font-size: x-small;">Cloud storage services like Dropbox and Google Drive are widely used, but security and privacy are often cited as serious concerns. One solution is to encrypt data on the client devices before it is uploaded, but this introduces a "key distribution" problem - how do you share the decryption key with all your devices? Some services sidestep this issue by deriving these keys from your password, but this does not offer much security since passwords are notoriously easy to guess. To overcome this challenge, we have developed <a href="https://ssg.aalto.fi/projects/omnishare/" target="_blank">OmniShare</a>, the first system to combine client-side encryption using high-entropy keys with a suite of secure, yet intuitive, key distribution mechanisms. Based on your devices' capabilities, OmniShare automatically selects the best mechanism to transfer your decryption key securely between your devices. All you have to do is scan a QR code or bring your devices close enough for them to communicate over an ultrasonic channel. OmniShare also allows you to share individual encrypted files with other users. OmniShare is open source software and a beta version is currently available (for closed beta testing) on Windows and Android.</span></i><br />
<i><br /></i><i><br /></i><i>How secure is your cloud storage? </i><br />
<br />
Cloud storage services, such as Dropbox and Google Drive, are increasingly being used by individuals and businesses. A <a href="http://ec.europa.eu/eurostat/statistics-explained/index.php/Internet_and_cloud_services_-_statistics_on_the_use_by_individuals" target="_blank">2015 EU survey</a> showed that at least one in every five people in Europe use cloud storage services. The two main benefits cited by users are the abilities to 1) use files from several devices or locations and 2) easily share files with other users. However, of the respondents who chose not use cloud services, 44% said that security and privacy were important concerns.<br />
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<b>Who has your data?</b> All major cloud storage providers use a variety of good security measures to protect your data. For example, they encrypt the data in transit as it is uploaded and downloaded, and they also encrypt the data at rest while it is stored on their servers. However, the cloud providers themselves still have access to this data. Even if the provider is completely trustworthy, this still increases the risk of your data falling into the wrong hands. For example, if the provider suffers a data breach, will your data be secure? Some of the provider's staff have legitimate access to the data for development or maintenance purposes, but what could a disgruntled employee do with this access? Depending on where the cloud provider is located, could the provider be legally forced to disclose your data?<br />
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<b>Client-side encryption:</b> Encrypting data on the users' devices before uploading it to the cloud is an effective way to mitigate these risks. However, users want this data to be accessible from all their devices. For example, if Alice encrypts a file on her PC and uploads it to the cloud, she also wants to access it from her smartphone. If this file is encrypted, her smartphone must have (or be able to obtain) the relevant decryption key. Naturally, these keys should not be managed by the cloud service provider due to the risks described above. So now we have a <i>key distribution problem</i>: how can Alice securely distribute her decryption key to all her devices?<br />
<br />
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEidZ4BiXPRsWBt37haKBQ7ExeDVtggLLsk9ivcTu0qm0chGhmnfHkBvaunMgjGc7GWTFpj-HbeBlRDOqZS1VcSiYeZ5Gh12YehT9hqXFom4o_CIMcOG9u7hC1CMIVpb_WMGyuGQD5lQPc2l/s1600/OmniShare-file_browser2.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEidZ4BiXPRsWBt37haKBQ7ExeDVtggLLsk9ivcTu0qm0chGhmnfHkBvaunMgjGc7GWTFpj-HbeBlRDOqZS1VcSiYeZ5Gh12YehT9hqXFom4o_CIMcOG9u7hC1CMIVpb_WMGyuGQD5lQPc2l/s320/OmniShare-file_browser2.png" width="182" /></a><br />
<br />
<b>The problem with passwords:</b> Current encrypted storage services like <a href="https://spideroak.com/" target="_blank">SpiderOak</a> and <a href="https://tresorit.com/" target="_blank">Tresorit</a> sidestep the key distribution problem by deriving keys from the user's password using a deterministic password-based key derivation function (PBKDF). Both Alice's PC and smartphone can derive the same key from Alice's password. However, it is well-known that human-chosen passwords are relatively easy to guess, so this approach does not provide very strong security guarantees. To avoid deriving keys from weak passwords, services like <a href="https://viivo.com/" target="_blank">Viivo</a>, <a href="https://www.boxcryptor.com/en" target="_blank">BoxCryptor</a>, and <a href="https://www.sookasa.com/" target="_blank">Sookasa</a> use additional servers to manage and distribute keys, but this adds cost and introduces new vulnerabilities.<br />
<br />
To address these challenges, we have developed a new system called <i><a href="https://ssg.aalto.fi/projects/omnishare/" target="_blank">OmniShare</a>. </i>Here is a <a href="https://vimeo.com/158914208">two minute summary</a> of what OmniShare is. Read on for a longer explanation.<br />
<br />
OmniShare runs as a client-side app on each of your devices. When you upload a file to your cloud storage (e.g. Dropbox) using OmniShare, the app automatically encrypts the file with a strong (i.e. high entropy) key. Since the files are encrypted on your own device, there is no longer a risk of your cloud provider losing, leaking or disclosing your data. The encryption keys are securely generated and stored on your own device. For additional security, these keys can be kept in a hardware-backed key-store or protected by a <a href="https://ssg.aalto.fi/projects/virtual-tee/" target="_blank">Trusted Execution Environment</a> (TEE).<br />
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<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiqt8JzAFV12KS4TDYG2YAgqfGK_jSKRrgtlYBILK3dJ3lg9LCDGHqFvJdZXFaXuof8AIFLdjrhQMDm42OaIp-kADvQmL9gzpzbxEqstb3bksob1eAiLaQZgius-g604j7dAkwabd_VRKXm/s1600/OmniShare-QR_code2.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiqt8JzAFV12KS4TDYG2YAgqfGK_jSKRrgtlYBILK3dJ3lg9LCDGHqFvJdZXFaXuof8AIFLdjrhQMDm42OaIp-kADvQmL9gzpzbxEqstb3bksob1eAiLaQZgius-g604j7dAkwabd_VRKXm/s320/OmniShare-QR_code2.png" width="182" /></a></div>
<b>Key distribution:</b> To solve the key distribution problem, OmniShare provides a suite of mechanisms to securely transfer your keys between your devices. These mechanisms are based on the insight that you can bring your own devices physically close together in order to establish an out-of-band (OOB) communication channel. For example, if one of your devices has a screen and the other has a camera, OmniShare can display a QR code on the first device and ask you to scan it from the second. If one device has a speaker and the other has a microphone, OmniShare can pair your devices using ultrasonic communication (the <a href="http://arstechnica.com/gadgets/2014/06/chromecast-will-talk-to-smartphones-using-ultrasonic-tones/" target="_blank">same technique</a> that is used in the Google Chromecast devices). OmniShare automatically chooses the best mechanism based on your devices' capabilities. The majority of the communication takes place via the cloud storage service itself, but the local OOB communication between devices ensures that the key is transferred to the correct device. This approach is both usable and secure and, as you can see, avoids the problem with passwords.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhsEftH-XH5Pm_icflrOCw4HcPpPDODUBbTuqh0HKlNm9geigWW4A3AxwxokzOsgRbBapS9dd-1BQikoIHAHpL8DmCj41mcT_MUjVHMF96llu9IwzNwxp-YRDW_d3VsdBk7KSqYMfHKy4gQ/s1600/OmniShare-sharing2.png" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhsEftH-XH5Pm_icflrOCw4HcPpPDODUBbTuqh0HKlNm9geigWW4A3AxwxokzOsgRbBapS9dd-1BQikoIHAHpL8DmCj41mcT_MUjVHMF96llu9IwzNwxp-YRDW_d3VsdBk7KSqYMfHKy4gQ/s320/OmniShare-sharing2.png" width="180" /></a><b>Sharing:</b> To support the full range of cloud storage functionality, OmniShare also allows you to share selected files with your friends and colleagues. The files remain encrypted throughout this process, and OmniShare securely transfers the decryption keys to the other user. Again, OmniShare provides a suite of intuitive mechanisms for achieving this, including: Bluetooth, Near Field Communication (NFC), and ultrasonic communication.<br />
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<b>Open source security:</b> OmniShare is open source software available under the Apache 2.0 license. It currently exists for Windows and Android, and an iOS version is in the works. We plan to open a closed beta test soon.<br />
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OmniShare was chosen as the overall winner in the <a href="http://mapping-competition.uni-hannover.de/winners.html">MAPPING Privacy via IT Security competition</a> and was demonstrated at <a href="http://www.cebit.de/exhibitor/aalto-university-foundation/A372843">CeBIT 2016</a>.<br />
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Here is a <a href="https://vimeo.com/158914208">video explaining OmniShare</a>.<br />
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You can find more information and register to participate in the beta test from our official project page: <a href="https://ssg.aalto.fi/projects/omnishare/">https://ssg.aalto.fi/projects/omnishare/</a><br />
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We have also published a technical report on arXiv describing the full details of this system: <a href="http://arxiv.org/abs/1511.02119">http://arxiv.org/abs/1511.02119</a><br />
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For a brief overview of OmniShare, have a look at our slides from CeBIT 2016: <a href="https://ssg.aalto.fi/omnishare/download/OmniShare-Overview.pdf">https://ssg.aalto.fi/omnishare/download/OmniShare-Overview.pdf</a><br />
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Development of OmniShare was supported by the <a href="http://www.aka.fi/en">Academy of Finland</a> (via the <a href="https://wiki.aalto.fi/display/CloSeProject/CloSe+Project+Public+Homepage">CloSe project</a>) and the <a href="http://www.icri-sc.org/">Intel Collaborative Research Institute for Secure Computing</a>.<br />
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OmniShare was initially developed as part of <a href="https://aaltodoc.aalto.fi/bitstream/handle/123456789/19048/master_Nguyen_Long_2015.pdf">Nguyen Hoang Long's MSc thesis</a>.<br />
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Unknownnoreply@blogger.com2tag:blogger.com,1999:blog-3151476947673617441.post-34190545954741331662016-02-15T10:32:00.001+02:002017-10-18T08:49:09.912+03:00Zero-effort authentication : useful but difficult to get right<i><br /></i><i><span style="font-size: x-small;">At the forthcoming <a href="http://www.internetsociety.org/events/ndss-symposium-2016">2016 Networking and Distributed Systems Symposium </a>we present case study on designing systems that are simultaneously secure and easy-to-use. Transparent ("zero-effort") authentication is a very desirable goal. But designing and analyzing them correctly is a challenge. At the 2014 IEEE Security and Privacy Symposium, a top conference on information security and privacy, researchers from Dartmouth presented a simple and compelling scheme called ZEBRA for continuously but transparently re-authenticating users. Through systematic experiments, we show that a hidden design assumption in ZEBRA can be exploited by a clever attacker. More technical details in the <a href="http://arxiv.org/abs/1505.05779">extended research report</a> and in our <a href="https://se-sy.org/projects/bilateral-auth/zebra/">project page</a>.</span></i><br />
<i><br /></i>[With co-author and guest blogger <a href="http://saxena.cis.uab.edu/">Nitesh Saxena (University of Alabama at Birmingham)</a>]<br />
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Computing systems are pervasive. We need to authenticate ourselves to them all the time. All of us know the tales of woe trying to manage zillions of passwords. But unwieldy authentication and access control systems are not limited to computing -- we struggle with multiple keys, loyalty cards and so on. This is what makes attempts to design transparent "zero-effort" authentication systems very compelling. The adjectives "transparent" and "zero effort" refer to the fact that the system is able to authenticate users without requiring them to do anything special just for the purpose of authentication. There has been a lot of work in trying to get the design of zero-effort authentication systems right and the security community has been making progress. But getting usability and security right is a difficult challenge.<br />
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<b>Transparent de-authentication: </b>Two years ago, <a href="http://cacm.acm.org/news/178946-dartmouths-new-zebra-bracelet-strengthens-computer-security/fulltext">researchers from Dartmouth college described a system called ZEBRA</a> at <a href="http://www.ieee-security.org/TC/SP2014/program-notabs.html#43">IEEE Security and Privacy Symposium</a>, one of the top four venues for academic security research. ZEBRA was intended to solve the problem of prompt, user-friendly <i>deauthentication. </i>Think about what happens when you stop interacting with your phone or PC and walk away. What you really want is your device to lock itself or log you out promptly. This is what deauthentication is. It is the means to defend against accidental or intentional misuse of your device by someone else when you walk away while leaving it open.<br />
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Typically deauthentication is based on idle time limits: if you have not interacted with your device for a period exceeding some pre-set time limit, it will deauthenticate you. But therein lies the rub: if the time limit is too short, you will be annoyed at having to log in frequently and unnecessarily; but if the time limit is too long, you risk that someone else may use your unattended device. Transparent zero-effort schemes have been tried out before. One is <a href="https://sourceforge.net/projects/blueproximity/">BlueProximity </a>which allows you to pair your Bluetooth-enabled phone with your PC. Thereafter your PC will use Bluetooth to sense your phone's presence in the vicinity and will automatically lock if it can no longer sense your phone. <a href="https://sourceforge.net/projects/blueproximity/">BlueProximity </a>itself is not very secure (an attacker who knows the Bluetooth address of your phone can easily break <a href="https://sourceforge.net/projects/blueproximity/">BlueProximity</a>) but it is an example of the class of zero-effort authentication solutions that rely on proximity detection via short-range wireless channels. Another prominent example of this class of solutions is the collection of various <a href="https://en.wikipedia.org/wiki/Smart_key">"keyless access"</a> schemes found in high end cars. These systems have many problems: they are <a href="http://arxiv.org/abs/1505.05779">vulnerable to relay attacks</a>, and they are not effective in situations where people step away from their devices (and would thus want to be deauthenticated) but do not walk away far enough so that their devices can no longer sense their presence -- such situations are common in offices with cubicles or workstations in hospital wards and factories.<br />
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjgMVHH6r_4W9D5bkVr0SBxD8fIzMFcygGtAZ-Q78GsmFe1deZ48EmS6eH3PrcyEk9V_2m_MfyVwIlEoCMqXGkbA73Xw9gWfd3wBTVonbiOuNsufB8Z-p4qwWR2db3jaRQU-vMqbjYIln8/s1600/zebra-attack.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="224" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjgMVHH6r_4W9D5bkVr0SBxD8fIzMFcygGtAZ-Q78GsmFe1deZ48EmS6eH3PrcyEk9V_2m_MfyVwIlEoCMqXGkbA73Xw9gWfd3wBTVonbiOuNsufB8Z-p4qwWR2db3jaRQU-vMqbjYIln8/s640/zebra-attack.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">The ZEBRA approach to deauthentication</td></tr>
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<b>ZEBRA: </b>This is where ZEBRA comes in with a very simple and elegant solution to the problem. In ZEBRA every user is required to wear a Bluetooth-enabled "bracelet" equipped with an accelerometer and a gyroscope in his/her dominant hand. As a ZEBRA user, when you first log into a device ("terminal" in ZEBRA terminology), it establishes a secure connection to your bracelet. Thereafter while you interact with your terminal, e.g., using its keyboard or mouse, the bracelet will send the measurements generated by your interactions over to the terminal which then uses a machine learning classifier to map them into a sequence of predicted interactions. Now your terminal has two different views of the same phenomenon of you interacting it: the first is the sequence of interactions it can directly observe via its peripherals like keyboard and mouse; the second is the sequence of predicted interactions inferred from the measurements sent over from the bracelet. Now you can perhaps see the logic behind ZEBRA: if the two sequences match, ZEBRA can conclude that the person who is interacting with it is the same person who is wearing the right bracelet for the current login session; if the sequences diverge, ZEBRA can promptly deauthenticate the current login session. ZEBRA calls this "Bilateral Recurring Authentication" -- you can think of it as the <a href="https://en.wikipedia.org/wiki/Rashomon">Rashomon </a>approach to deauthentication! It is simple, elegant, and, unlike biometric authentication, does not depend on the particular behaviour of the user being authenticated.<br />
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<b>Hunting ZEBRA: </b>Is ZEBRA secure? To evaluate this, authors of ZEBRA conducted experiments in which participants were asked to act as attackers. A researcher, acting as the victim, left the terminal ("terminal A") with an open login session moved to another terminal ("terminal B") and started interacting with it. The job of the "attacker" was to watch what the victim was doing on terminal B, mimic the same interactions on terminal A and try to avoid being logged out of terminal A by ZEBRA. Security 101 teaches you not to under-estimate the capabilities of attackers. Realizing this, ZEBRA authors arranged to give a generous handicap to their attackers: the victims actually announced aloud what they are about to do ("typing", "scrolling" etc.). The experiments showed that ZEBRA was able to deauthenticate <i>all</i> attackers even with this extra help.<br />
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjSRCpueGJiyvOh3uSZIK7xn_1zSsLa8DWJ6sfdTqwQRVEq9tx4QcFDM9PFHVSC2sV95m_a57Bxr57QwIijhEavsLKqTtrTQEoHbGPEIM-7IhnoADtUwBL4lnGah4q_gB04RUbTmPZ_F4U/s1600/zebra-attack.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="144" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjSRCpueGJiyvOh3uSZIK7xn_1zSsLa8DWJ6sfdTqwQRVEq9tx4QcFDM9PFHVSC2sV95m_a57Bxr57QwIijhEavsLKqTtrTQEoHbGPEIM-7IhnoADtUwBL4lnGah4q_gB04RUbTmPZ_F4U/s640/zebra-attack.png" width="640" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Evaluating security of ZEBRA experimentally</td></tr>
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We realized that there is a problem here: in the experiments, the "attackers" were asked to mimic <i>everything </i>that the victim was doing; is that the best strategy for the attackers? It turned out that ZEBRA was doing its bilateral comparison only when the terminal perceived interactions via its peripherals. But the terminal being attacked (terminal A) is under the control of the attacker. Therefore we conjectured that a clever attacker does not need to mimic everything that the victim was doing but can <i>opportunistically</i> choose to mimic only those interactions where he has a good chance of passing ZEBRA's bilateral comparison test!<br />
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To test our theory, we needed an implementation of ZEBRA. Since the authors of ZEBRA did not have an implementation to share with us, we built our own end-to-end implementation from scratch using standard off-the-shelf Android Wear smartwatches. We then did a series of systematic experiments modeling different types of opportunistic attackers. For example, our opportunistic <i>keyboard-only </i>attacker ignores everything else except keyboard interactions (since he can do a lot of damage to all modern operating systems using only keyboard interactions). In our experiments, the test participants were victims while our researchers played the role of attackers.<br />
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<tr><td style="text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjYMZkQsQPV4Am6G3GYM-2O6kO9xLzlDFCQ8ODGnbt2vj3Qmen1-ulLYmHVoeA40Ea1JAO41fbna0KX3_NZJX5ZzXNQChMw4YQDQOYbDEIsHUD1lpXkHRpk4pkfDFHRTDtmYZhhh2lZtkI/s1600/results.png" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="176" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjYMZkQsQPV4Am6G3GYM-2O6kO9xLzlDFCQ8ODGnbt2vj3Qmen1-ulLYmHVoeA40Ea1JAO41fbna0KX3_NZJX5ZzXNQChMw4YQDQOYbDEIsHUD1lpXkHRpk4pkfDFHRTDtmYZhhh2lZtkI/s400/results.png" width="400" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">Opportunistic attackers can circumvent ZEBRA</td></tr>
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Our results show that while ZEBRA is indeed effective against naive attackers, it can be circumvented by opportunistic attackers. In the figure above, the graph on the left shows that ZEBRA deauthenticates all naive attackers. The graph on the right shows that opportunistic attackers evaded detection 40% of the time. The red and blue lines correspond to two different configurations of ZEBRA.<br />
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The fundamental problem in the design of ZEBRA was that the deauthentication decision was subject to an input source (interactions on terminal A) that was <i>under the control of the attacke</i>r! We can see this as a case of <a href="https://xkcd.com/327/">tainted input</a>, which is familiar to computer scientists. Well-known <a href="https://www.owasp.org/index.php/Input_Validation_Cheat_Sheet">defenses against tainted input</a> can be adapted to strengthen the design of ZEBRA.<br />
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<b>Summary: </b>There are two messages here:<br />
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<li>Transparent, "zero-effort" authentication, which works by taking the user out of the loop of authentication decisions, is important. The need for it is demonstrated by already available production solutions that attempt to provide zero-effort authentication. But designing them correctly is subtle and difficult.</li>
<li>Modeling the adversary correctly and realistically is a fundamental challenge in the analysis of security schemes, especially when novel approaches for balancing usability and security are introduced, as in the case of ZEBRA.</li>
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We showed that under a more realistic adversary model, ZEBRA, as it was originally designed, is not secure against malicious attackers. It is still an effective defense against accidental misuse. The fundamental problem in the design of ZEBRA is a case of tainted input -- we are currently developing ways of improving ZEBRA.<br />
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More detailed information can be found in <a href="https://www.internetsociety.org/sites/default/files/blogs-media/pitfalls-designing-zero-effort-deauthentication-opportunistic-human-observation-attacks.pdf">our NDSS paper</a> or our <a href="http://arxiv.org/abs/1505.05779">extended research report</a>. All figures in this post are taken from our paper/report.<br />
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This work was supported by the <a href="http://www.aka.fi/en">Academy of Finland</a> (via the Contextual Security project) and the US <a href="http://www.nsf.gov/">National Science Foundation</a>.<br />
<br />Unknownnoreply@blogger.com0tag:blogger.com,1999:blog-3151476947673617441.post-21939771690636735862015-10-27T21:41:00.000+02:002017-10-18T08:49:36.084+03:00Practical attacks against 4G (LTE) access network protocols<div dir="ltr" style="text-align: left;" trbidi="on">
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<span style="background-color: #f9f9f9; color: #252525; font-family: sans-serif; font-size: 12.3704px; line-height: 17.3186px;"><br /></span><span style="background-color: #f9f9f9; color: #252525; font-family: sans-serif; font-size: 12.3704px; line-height: 17.3186px;"><br /></span><span style="font-size: xx-small;"><span style="background-color: #f9f9f9; color: #252525; font-family: sans-serif; line-height: 17.3186px;">(C</span><span style="background-color: #f9f9f9; color: #252525; font-family: sans-serif; line-height: 17.3186px;">ountries with commercial LTE service as of December 7, 2014 shaded in red; </span>Source <a href="https://en.wikipedia.org/wiki/LTE_(telecommunication)">Wikipedia article</a>)</span><br />
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<i>4G mobile communication networks, also known as "LTE" (Long Term Evolution) are widely deployed. How good is LTE security? <a href="https://twitter.com/raviborgaonkar">Ravishankar Borgaonkar</a>, a postdoc in <a href="http://cs.aalto.fi/secure_systems/">my research group</a>, and Altaf Shaik, a doctoral student at TU Berlin, discovered several surprising lapses in 4G security standards and baseband chipsets which they will <a href="http://blackhat.com/eu-15/briefings.html#lte-and-imsi-catcher-myths">demonstrate</a> at the forthcoming <a href="http://t2.fi/schedule/2015/#speech11">T2 Security Conference 2015</a> & <a href="http://blackhat.com/eu-15/">Black Hat Europe 2015</a>. We will also be presenting an academic paper on the topic at the prestigious Internet Society <a href="https://www.internetsociety.org/events/ndss-symposium-2016">NDSS conference</a> in February 2016. More technical details in our arXiv report (see our <a href="https://se-sy.org/projects/netsec/lte">project page</a>)</i><br />
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Security in mobile communications networks has been improving in every generation. 3G introduced mutual authentication that made it a lot harder to mount fake base station attacks (such as those used in <a href="https://en.wikipedia.org/wiki/IMSI-catcher">IMSI catchers</a>). 4G tightened up many signaling protocols by requiring authentication and integrity protection for them. The generally held belief has been that 4G security is robust and in particular, fake base station attacks mostly difficult to mount.<br />
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Over the last several months Ravishankar Borgankar and Altaf Shaik, working with me, <a href="https://www.cs.helsinki.fi/en/people/pvniemi">Valtteri Niemi (University of Helsinki)</a> and <a href="https://www.isti.tu-berlin.de/?id=70771">Jean-Pierre Seifert (TU Berlin)</a>, painstakingly experimented with several LTE devices in a laboratory setting and uncovered two classes of vulnerabilities:<br />
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<b>Leaking information about user locations: </b>Already when 2G (GSM) networks were being designed, location privacy was considered important. When a mobile device attaches to a network, it is given a temporary identifier (known as TMSI - Temporary Mobile Subscriber Identity). All the signaling messages between the mobile device and the network will thereafter refer only to the TMSI, rather than the user's permanent identifiers (such as phone numbers or IMSIs - International Mobile Subscriber Identity). TMSIs are random and updated frequently (e.g., whenever the mobile device moves to a new location area). The idea is that an attacker passively monitoring radio communication would not be able to link TMSIs to permanent identifiers or track movements of a given user (since his TMSIs would change over time). A couple of years ago, <a href="http://www.internetsociety.org/location-leaks-over-gsm-air-interface">Dennis Foo Kune and Yongdae Kim showed</a> that an attacker in a 2G (GSM) network can trigger a paging request (by sending a silent text message or initiating and quickly terminating a call) to be sent to a target user with a given phone number. Paging requests contain TMSIs. The attacker can thereby link TMSIs to phone numbers.<br />
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We discovered that paging requests can be triggered in a new and surprising manner -- via social network messaging apps. For example, if someone who is not your Facebook friend sends you an instant message, Facebook will silently put it in the <a href="https://www.facebook.com/help/188872764494245">"Other" folder</a> as a spam protection mechanism (unless the spammer pays Facebook 1€!). If you have Facebook Messenger installed on your LTE smartphone, incoming messages, including those destined to the Other folder, will trigger a paging request, allowing a passive attacker to link your TMSI to your Facebook identity and track your movements. To make matters worse, we noticed that TMSIs are not changed sufficiently frequently -- in one urban area TMSIs assigned by multiple mobile carriers persisted up to three days! In other words, once the attacker knows your TMSI, he can passively track your movements for up to three days.<br />
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An active attacker using a fake base station can do even better. LTE access network protocols incorporate various reporting mechanisms that allow the network to troubleshoot faults, recover from failures and assist mobiles in handovers. For example, after a failed connection, a base station can ask an LTE device to provide a failure report with all sorts of measurements including which base stations are seen by the device and with what signal strength. Once an attacker grabs such a report, he can use the information to triangulate the device's location. In fact, at least one device we tested even reported its exact GPS location. Failure recovery mechanisms are essential to the reliable operation of large mobile networks -- LTE designers had a difficult design trade-off between potential loss of user privacy and ensuring network reliability.<br />
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<b>Denial of service: </b>Imagine that you travel to a different country but your mobile subscription does not include roaming. When your phone tries to connect to the network at your destination, it will be rejected with an appropriate "cause number" (such as "ROAMING NOT ALLOWED"). Your phone will dutifully accept this directive and <i>will not</i><b style="font-style: italic;"> </b>attempt to connect again until you re-initialize the device (e.g., by rebooting it). This is so that your device does not waste its battery in vain by repeatedly trying to connect to a network only to be rejected. It is also a way to minimize unnecessary signaling over the air. In other words, this design decision was also motivated by a desire to trade-off in favor of reliability and performance. You can guess the rest: we show, for example, that an attacker can deny 4G and 3G services to a 4G device, thereby effectively downgrading it to 2G. Once downgraded, the device is open to all the legacy 2G vulnerabilities. The parameter negotiation during the LTE connection set up process is vulnerable to bidding down attacks by a man-in-the-middle who can fool an LTE device and an LTE network into concluding that they can only communicate using 2G.<br />
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The equipment for our attacks (<a href="http://www.ettus.com/product/details/UB210-KIT">a USRP board</a> and antennas, along with open source software <a href="http://openlte.sourceforge.net/">openLTE</a>) is inexpensive and readily available. It costs a little over 1000 €!<br />
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Why do these vulnerabilities exist in the first place? A likely reason is that designing a complex system like LTE is a complicated task that calls upon the designers to make several difficult trade-offs (such as the privacy vs. availability trade-off we mentioned above). The trade-offs embedded in the design of LTE were made in the late 2000s when LTE was being designed. Several years on, the equilibrium points in the trade-offs have changed, making the kinds of attacks like ours very practical and economically viable for a potential attacker. We recommend that future standardization incorporate agility of trade-offs in the face of such changes. Software-defined networking will make it easier to build in such agility.<br />
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We discovered the vulnerabilities and attacks earlier this year. We notified affected manufacturers and carriers during June and July. We have been following the standard responsible disclosure practices and waited till the end of the 90-day waiting period before publicising the attacks. We have also been working with relevant standards organizations to address these vulnerabilities. Several manufacturers have already issued patches. 3GPP, the body that standardizes LTE, has initiated several specification changes to address the vulnerabilities we discovered.<br />
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Our arXiv technical report has more technical details. <i>(see our <a href="https://se-sy.org/projects/netsec/lte">project page</a>)</i></div>
Unknownnoreply@blogger.com1