Reliable Estimation of Differential Privacy under Selection Bias
Correcting for selection bias in differential privacy audits yields more reliable lower bounds on actual privacy leakage — a methodological improvement for DP-dependent compliance claims.
Summary written by editorial AI · Source link below
arXiv:2610.27021v1 Announce Type: new Abstract: Statistical privacy audits are essential for verifying the theoretical privacy guarantees of differentially private (DP) mechanisms. Auditors typically use Membership Inference Attacks (MIAs) to estimate a lower bound on the true privacy leakage of DP mechanisms at a specified confidence level, which should not exceed the theoretical privacy upper bound guaranteed by DP. However, we found that many auditing implementations in prior work introduce
Editorial Analysis
Enterprises relying on differential privacy for GDPR compliance should be aware that standard auditing methods may underestimate true privacy leakage due to selection bias.
Ask your data science team to verify that differential privacy audits account for selection bias before citing them in compliance documentation.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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