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<!DOCTYPE HTML>
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https://pages.nist.gov/genomics_ppfl/
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<html>
<head>
<title>Genomics PPFL Platform: 2025 Red Teaming Event</title>
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<meta property="og:title" content="Genomics PPFL Platform: 2025 Red Teaming Event"/>
<meta property="og:description"
content="The NIST Privacy Engineering Program has launched the Genomics PPFL Platform: 2025 Red Teaming Event
to direct the research community to explore the complex interaction between model utility, privacy interventions
such as noise and clipping bounds, data distribution, and resilience to membership and data reconstruction
attacks. This will inform consensus guidance on privacy for federated learning collaborations between genomics
researchers." />
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<li><a href="pages/problem.html" class="button">Problem Statement</a></li>
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<br>
<h2>Genomics PPFL Platform</h2>
<h2>2025 Red Teaming Event</h2>
<p>Welcome to the homepage of the Genomics PPFL Platform.
</p>
<ul class="actions special">
<li><a href="pages/problem.html" class="button">Problem Statement</a></li>
<!-- <li><a href="pages/leaderboard.html" class="button">Leaderboard </a></li> -->
<li><a href="pages/submit.html" class="button new">Submit</a></li>
<!-- <li><a href="pages/techniques.html" class="button">Techniques</a></li> -->
<li><a href="pages/resources.html" class="button">Resources</a></li>
<li><a href="pages/connect.html" class="button">Connect</a></li>
</ul>
</section>
<!-- Main -->
<section id="main" class="container">
<section class="box special">
<header class="major">
<h2>NIST launches new privacy red-teaming tasks. <br>Will you accept the challenge?
</h2>
<p>The
<a href="https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering">
NIST Privacy Engineering Program</a> and the <a href="https://www.nccoe.nist.gov"> National Cybersecurity Center of Excellence </a> are pleased to
invite the public to participate in privacy red-teaming exercises. Privacy-enhancing technologies such
as synthetic data and federated-learning promise to unlock sensitive data stores for research and machine
learning while limiting exposure of private information. Come help us test the efficacy of PETs applied
to two model problems.
</p>
<p style="font-size:24px;">
<img src="./images/communication.png" height="30" style="vertical-align:middle"> 
<a href="https://meet.google.com/rxh-csab-jue">
Join our webinar</a>
introducing the opportunity 10 MAR 2025 at 11AM ET.
</p>
<p style="font-size:24px;">
<img src="./images/message.png" height="30" style="vertical-align:middle"> 
<a href="mailto:[email protected]">
Join the Genomics PPFL list-serv</a>
(submit an empty email to subscribe).
</p>
</header>
</section>
<div class="box">
<h3><!-- Model Problem 1: --> Federated learning over silos of genomic data</h3>
<p>Federated learning supports distributed research in cancer, biotech, and beyond, but privacy-preserving federated learning
(PPFL) is hard. We've implemented a differentially private PPFL network. Can you determine who is in the data and who is not?
Can you reconstruct sensitive feature values? What epsilon do you need to protect the individual records and client IP?
</p>
<p style="font-size:24px;">
<img src="./images/team.png" height="30" style="vertical-align:middle"> 
<a href="mailto:[email protected]">
Register your team</a>
to participate.
</p>
<h3>Project Information</h3>
Teams are welcome to register at any point during the exercise and submit an entry for any problem.
Participating in Phase 1 isn't required for participating in Phase 2.
<br><br>
Phase 1, March-April 2025: Soybean Data
<br>
Phase 2, May-June 2025: Cancer Data
<br><br>
<ul>
<li>Check out the <a href="pages/problem.html">Problem Statements</a> tab to see the current red team problems!</li>
<li>Follow the steps on the <a href="pages/submit.html">Submit</a> tab to prepare your submission</li>
<li>Look at the <a href="pages/resources.html">Resources</a> tab for links to attack libraries you can try out, and other helpful references</li>
<li>Once the exercise is under way, you'll be able to see what techniques are being explored, and how participating teams are doing, on our Leaderboard and Techniques showcase.</li>
<li>Finally, see how to get in touch with us on the <a href="pages/connect.html">Connect</a> tab.</li>
</ul>
<!--
<h3>Model Problem 2: Differentially private synthetic demographic data</h3>
<p>
Differentially private synthetic data is an increasingly common way to release statistics. The Collaborative Research Cycle
(CRC) has since benchmarked nearly 500 ‘de-identified’ synthetic data samples. But are they really deidentified? Or could
you re-identify them (reconstruct targeted individuals’ correct attribute values) if you really tried? Here's your chance!
</p>
-->
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<br>
<h3>Questions</h3>
<p >Please contact NIST scientist
<a href="mailto:[email protected],[email protected]?subject=[Genomics PPFL Platform: Red Team Event]">Gary Howarth</a>.
</p>
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