Characterizing Privacy-Audit Alignment in Behavioral Audit of Machine Unlearning
Read the original on arXiv Machine Learning →The paper investigates the privacy risks inherent in auditing machine unlearning (MU) when the audit relies only on querying the model for behavioral signals. It shows that such generic audit schemes inevitably leak information about the retained data set, providing a geometric transfer theorem that bounds the distinguishability of retained set membership based on audit accuracy. The study also analyzes how the unlearned set, target sample, and query protocol influence the privacy‑audit transfer coefficient, with empirical evidence from both convex and non‑convex models supporting the theoretical findings.
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