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Hardening Confidential Federated Compute against Side-channel Attacks

arXiv Security Archived Mar 24, 2026 ✓ Full text saved

arXiv:2603.21469v1 Announce Type: new Abstract: In this work, we identify a set of side-channels in our Confidential Federated Compute platform that a hypothetical insider could exploit to circumvent differential privacy (DP) guarantees. We show how DP can mitigate two of the side-channels, one of which has been implemented in our open-source library.

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    Computer Science > Cryptography and Security [Submitted on 23 Mar 2026] Hardening Confidential Federated Compute against Side-channel Attacks James Bell-Clark, Albert Cheu, Adria Gascon, Jonathan Katz In this work, we identify a set of side-channels in our Confidential Federated Compute platform that a hypothetical insider could exploit to circumvent differential privacy (DP) guarantees. We show how DP can mitigate two of the side-channels, one of which has been implemented in our open-source library. Comments: 18 pages Subjects: Cryptography and Security (cs.CR); Data Structures and Algorithms (cs.DS) Cite as: arXiv:2603.21469 [cs.CR]   (or arXiv:2603.21469v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.21469 Focus to learn more Submission history From: Albert Cheu [view email] [v1] Mon, 23 Mar 2026 01:13:17 UTC (303 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-03 Change to browse by: cs cs.DS References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Mar 24, 2026
    Archived
    Mar 24, 2026
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