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News/Detectability without Interference: Efficient Canary Design for Privacy Auditing in One Run
arxiv
PublishedOctober 16, 2026 at 4:00 AM

Detectability without Interference: Efficient Canary Design for Privacy Auditing in One Run

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Publisher summary· verbatim

arXiv:2605.27292v3 Announce Type: replace Abstract: Privacy auditing aims to empirically assess privacy leakage in machine learning models using membership inference attacks (MIAs), and to derive lower bounds on differential privacy (DP) parameters. Standard privacy auditing inserts a sensitive samp

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