Detectability without Interference: Efficient Canary Design for Privacy Auditing in One Run
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 sample (a canary) into many ($\gg 100$) randomized training runs, requiring the auditor to detect whether it was included in each run. In one-run auditing, many canaries are included in a single training run, avoiding the high computational cost of multi-run approaches. However, recent theory shows that canaries can interfere with one another, reducing per-canary MIA scores and thereby weakening the audit power. We propose IBIS, a framework that opt...
| Authors | Mathieu Dagréou, Valentin Dorseuil, Mohammad Yaghini et al. |
|---|---|
| Primary category | cs.LG |
| Published | 26 May 2026 |
| Last updated at source | 15 October 2026 |
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