SoK: Privacy Attacks on Machine Learning via Explainable AI
Read the original on arXiv Machine Learning →The paper surveys 25 studies that use explainable AI to compromise machine learning models, covering attacks such as model extraction, membership inference, and model inversion. It distinguishes between how explanations are obtained—through target releases, attacker-derived methods, secondary disclosure, privileged access, or global artifacts—and shows that explanations can lower extraction costs and reveal membership signals via statistics, recourse distance, and robustness. The authors compare threat models, signals, and defenses, concluding that no single explanation type is always unsafe and that protection must be tailored to the specific acquisition path and target asset.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.