SoK: Privacy Attacks on Machine Learning via Explainable AI
Machine learning explanations reveal model behavior beyond predictions, creating attack surfaces for model confidentiality and data privacy. We systematize 25 studies that exploit explanations for model extraction, membership inference, and model inversion, treating attribute inference as partial inversion. Existing work is often labeled only black- or white-box, obscuring substantial differences in what explanation signal reaches an adversary. We therefore separate model knowledge from explanation acquisition and identify five paths: target-released, attacker-derived, secondary disclosure, privileged access, and released global artifacts. Across these paths, explanations reduce extractio...
| Authors | Abdullah Caglar Oksuz, Anisa Halimi, Erman Ayday |
|---|---|
| Primary category | cs.CR |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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