1159 lines
101 KiB
Text
1159 lines
101 KiB
Text
<!DOCTYPE html>
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<html lang="en" dir="ltr" prefix="content: http://purl.org/rss/1.0/modules/content/ dc: http://purl.org/dc/terms/ foaf: http://xmlns.com/foaf/0.1/ og: http://ogp.me/ns# rdfs: http://www.w3.org/2000/01/rdf-schema# schema: http://schema.org/ sioc: http://rdfs.org/sioc/ns# sioct: http://rdfs.org/sioc/types# skos: http://www.w3.org/2004/02/skos/core# xsd: http://www.w3.org/2001/XMLSchema# ">
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<head>
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<h1 class="nist-page__title">Privacy-Preserving Federated Learning – Future Collaboration and Continued Research</h1>
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<div class="daterange"><time datetime="2025-01-27T12:00:00Z">January 27, 2025</time>
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<span>By: </span>
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<a href="/blogs/cybersecurity-insights/authors/gary-howarth" hreflang="en">Gary Howarth</a> and <a href="/blogs/cybersecurity-insights/authors/sue-anie" hreflang="en">Sue Anie</a>
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<div class="nist-callout"><p class="paragraph"><em><span lang="EN">This post is the final blog in a </span></em><a href="/itl/applied-cybersecurity/privacy-engineering/collaboration-space/blog-series/privacy-preserving" data-entity-type="node" data-entity-uuid="559a91a1-527d-4e16-b39f-d00d5f230e8b" data-entity-substitution="canonical" title="Privacy-Preserving Federated Learning Blog Series"><em><span lang="EN">series on privacy-preserving federated learning</span></em></a><em><span lang="EN">. The series is a collaboration between NIST and the UK government’s Responsible Technology Adoption Unit (RTA), previously known as the Centre for Data Ethics and Innovation. Learn more and read all the posts published to date at NIST’s </span></em><a href="/itl/applied-cybersecurity/privacy-engineering/collaboration-space" data-entity-type="node" data-entity-uuid="7c2ce490-1c1a-47ad-b4ca-0d17d89a51bd" data-entity-substitution="canonical" title="Collaboration Space"><em><span lang="EN">Privacy Engineering Collaboration Space</span></em></a><em><span lang="EN"> or </span></em><a href="https://rtau.blog.gov.uk/tag/petsblogseries/"><em><span lang="EN">RTA’s blog</span></em></a><em><span lang="EN">. </span><span lang="EN-GB"> </span><span lang="EN-GB"></span></em></p></div><h2><span lang="EN-GB">Reflections and Wider Considerations</span></h2><p><span lang="EN-GB">This is the final post in the </span><a href="/itl/applied-cybersecurity/privacy-engineering/collaboration-space/blog-series/privacy-preserving" data-entity-type="node" data-entity-uuid="559a91a1-527d-4e16-b39f-d00d5f230e8b" data-entity-substitution="canonical" title="Privacy-Preserving Federated Learning Blog Series"><span lang="EN-GB">series</span></a><span lang="EN-GB"> that began with reflections and learnings from the first US-UK collaboration working with Privacy Enhancing Technologies (PETs). Since the </span><a href="https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/challenges/us-uk-pets-prize"><span lang="EN-GB">PETs Prize Challenges</span></a><span lang="EN-GB">, the ecosystem around these technologies has continued to develop, with a shift from more theoretical and academic conversations to greater uptake and consideration of PETs.</span><span lang="EN-GB"></span></p><p><span lang="EN-GB">Since our </span><a href="/blogs/cybersecurity-insights/uk-us-blog-series-privacy-preserving-federated-learning-introduction" data-entity-type="node" data-entity-uuid="86e0d988-feaf-40c6-84ef-9ad077de6fa6" data-entity-substitution="canonical" title="The UK-US Blog Series on Privacy-Preserving Federated Learning: Introduction "><span lang="EN-GB">first post in December of 2023,</span></a><span lang="EN-GB"> this series has explored a variety of practical considerations relevant to working with Privacy-Preserving Federated Learning (PPFL), ranging from understanding different types of </span><a href="/blogs/cybersecurity-insights/privacy-attacks-federated-learning" data-entity-type="node" data-entity-uuid="5012217a-db9e-4ce3-a243-66fce55899a2" data-entity-substitution="canonical" title="Privacy Attacks in Federated Learning"><span lang="EN-GB">privacy attacks</span></a><span lang="EN-GB"> and ways to mitigate their risks to individuals’ privacy, to exploring the importance of </span><a href="/blogs/cybersecurity-insights/protecting-model-updates-privacy-preserving-federated-learning" data-entity-type="node" data-entity-uuid="6447c18b-8287-4aa5-8d48-d7316ed0c595" data-entity-substitution="canonical" title="Protecting Model Updates in Privacy-Preserving Federated Learning"><span lang="EN-GB">input</span></a><span lang="EN-GB"> and </span><a href="/blogs/cybersecurity-insights/protecting-model-updates-privacy-preserving-federated-learning-part-two" data-entity-type="node" data-entity-uuid="2ab408ca-dad4-4013-a826-e46240f80485" data-entity-substitution="canonical" title="Protecting Model Updates in Privacy-Preserving Federated Learning: Part Two"><span lang="EN-GB">output</span></a><span lang="EN-GB"> privacy. Through recent posts, we also heard from guest contributors who were winners and judges in the PETs Prize Challenges. They shared thoughts and considerations for </span><a href="/blogs/cybersecurity-insights/scalability-challenges-privacy-preserving-federated-learning" data-entity-type="node" data-entity-uuid="53ed4c36-92ef-4ec6-b921-f8498c33809d" data-entity-substitution="canonical" title="Scalability Challenges in Privacy-Preserving Federated Learning"><span lang="EN-GB">scalability</span></a><span lang="EN-GB">, </span><a href="/blogs/cybersecurity-insights/implementation-challenges-privacy-preserving-federated-learning" data-entity-type="node" data-entity-uuid="44ee1aac-d52b-4f11-880b-193a955c3be4" data-entity-substitution="canonical" title="Implementation Challenges in Privacy-Preserving Federated Learning"><span lang="EN-GB">implementation</span></a><span lang="EN-GB">, </span><a href="/blogs/cybersecurity-insights/data-pipeline-challenges-privacy-preserving-federated-learning" data-entity-type="node" data-entity-uuid="d544d635-75f8-4350-b67a-5f60a3df96c2" data-entity-substitution="canonical" title="Data Pipeline Challenges of Privacy-Preserving Federated Learning"><span lang="EN-GB">data pipeline challenges</span></a><span lang="EN-GB">, and more.</span><span lang="EN-GB"></span></p><p><span lang="EN-GB">The scope of this blog series reflects the breadth of insights and considerations that emerged from the PETs Prize Challenges, but there are further aspects of working with PPFL that the challenges – and this series – have not addressed. This includes considerations for working with real data across multiple jurisdictions. PPFL can remove the need to transfer, store, or process real data centrally; this is useful when data sets must remain decentralized (for policy or technical reasons). However, we need research to inform our understanding of real-world applications of PPFL and PETs in general. NIST is researching the considerations and nuisances of real-world PPFL deployments through the PETs Testbed, hosted by the </span><a href="https://www.nccoe.nist.gov/"><span lang="EN-GB">NIST National Cybersecurity Center of Excellence (NCCoE)</span></a><span lang="EN-GB">. </span><span lang="EN-GB"></span></p><h2><span lang="EN-GB">Future Collaboration</span><span lang="EN-GB"></span></h2><p><span lang="EN-GB">As a relatively novel and emerging approach to working with data in a privacy-centric way, PPFL has the potential to support greater innovation and foster collaboration in the future.</span></p><p><span lang="EN-GB">The UK and US have agreed to further collaborative initiatives on PETs. Building on this commitment, and on insights from our previous collaboration</span><em><span lang="EN-GB">, </span></em><span lang="EN-GB">the UK National Disease Registration Service and the US National Cancer Institute are working together over the next few months to explore how PETs can drive innovative research into rare pediatric cancers through secure, privacy-preserving data collaboration between national disease registries. By using PETs, researchers can do cross-border data analysis without the need for data transfer or direct access; they can gain deeper insights without compromising data privacy. This activity is being supported by the UK’s Department for Science, Innovation and Technology, the White House's Office for Science and Technology Policy, NIST, and the US Department of Energy, and coordinated with the National Science Foundation.</span><span lang="EN-GB"></span></p><p><span lang="EN-GB">Collaboration using PETs, such as PPFL, will enable researchers to work around challenges that arise from the scarcity of data in individual countries. Using a federated approach as a mechanism for querying and/or modeling data will allow researchers to analyze data on rare pediatric cancers in a privacy-preserving way. This will also allow researchers to analyze information in ways that were previously not possible due to the limited availability of data (at present, no single jurisdiction has access to a large enough dataset encompassing ultra-rare tumor types to conduct such analysis on their own). </span></p><p><span lang="EN-GB">This approach also offers potential to scale research in the longer term, to include additional data from other jurisdictions in the analysis. Scaling to more countries could support a broader </span><a href="https://iccdp-parisconference.com/"><span lang="EN-GB">global initiative</span></a><span lang="EN-GB"> to foster collaboration on pediatric cancer.</span></p><h2><span lang="EN-GB">Continued Research</span></h2><p><span lang="EN-GB">The wider ecosystem around PPFL continues to develop, with the establishment of more forums for discussion and more opportunities for policymakers and researchers to align and collaborate on the horizon.</span></p><p><span lang="EN-GB"></span></p><p><span lang="EN-GB">To further investigate PETs and their respective suitability for specific use cases, NIST has launched the PETs Testbed. In collaboration with the </span><a href="https://www.xd.gov/team/" title="https://www.xd.gov/team/"><span lang="EN-GB">U.S. Census Bureau XD team</span></a><span lang="EN-GB"> and offered through the </span><a href="https://www.nccoe.nist.gov/" title="https://www.nccoe.nist.gov/"><span lang="EN-GB">NCCoE</span></a><span lang="EN-GB">, the first model problem is a PPFL model architecture with a genomics use-case. The model solution will allow us to explore the contours of the difficulty in deploying PPFL to solve real-world problems. NIST is creating privacy and utility metrology to support understanding of their relationship (e.g., tradeoffs) in the context of federated learning. The architecture will undergo a privacy threat evaluation that will involve the use of tools such as the </span><a href="https://www.nist.gov/privacy-framework/privacy-framework" title="https://www.nist.gov/privacy-framework/privacy-framework"><span lang="EN-GB">NIST Privacy Framework</span></a><span lang="EN-GB"> and the outcomes of a privacy red-teaming exercise. These assessments will serve as a framework for helping organizations navigate the trade-offs in a PPFL system. </span><span lang="EN-GB"></span></p><h2><span lang="EN-GB">Additional information</span></h2><p><span lang="EN-GB">As an emerging approach for working with data, there is still much to learn and explore about PPFL, and PETs more broadly, in theory and practice. The links below provide further information on this, including examples of real-world use cases:</span><span lang="EN-GB"></span></p><ul type="disc"><li><span lang="EN-GB"></span><a href="https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/testbed"><span lang="EN-GB">NIST PETs Testbed</span></a><span lang="EN-GB"></span></li><li><span lang="EN-GB"></span><a href="https://petsprizechallenges.com/"><span lang="EN-GB">PETs Prize Challenges</span></a><span lang="EN-GB"></span></li><li><span lang="EN-GB"></span><a href="https://www.gov.uk/government/publications/privacy-enhancing-technologies-cost-benefit-awareness-tool/cost-benefit-awareness-tool"><span lang="EN-GB">Costs and benefits associated with PETs</span></a><span lang="EN-GB"></span></li><li><span lang="EN-GB"></span><a href="https://www.oecd.org/en/topics/sub-issues/privacy-enhancing-technologies.html"><span lang="EN-GB">OECD information and resources on PETs</span></a><span lang="EN-GB"> </span></li></ul><p><span lang="EN-GB">If you would like to share feedback or additional ideas, please reach out to us at </span><span class="spamspan"><span class="u">pets</span> [at] <span class="d">dsit.gov.uk</span><span class="t"> (<span lang="EN-GB">pets[at]dsit[dot]gov[dot]uk</span>)</span></span><span lang="EN-GB"> or </span><span class="spamspan"><span class="u">privacyeng</span> [at] <span class="d">nist.gov</span><span class="t"> (<span lang="EN-GB">privacyeng[at]nist[dot]gov</span>)</span></span><span lang="EN-GB">. </span></p>
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