AI RESEARCH
Detectability in Diversity: Improved Canary Crafting for Privacy Auditing in One Run
arXiv CS.LG
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ArXi:2605.27292v1 Announce Type: new 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. Recent one-run auditing methods address the high cost of standard approaches by relying on a single