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The inference interface thus acts as a side channel for privacy leakage. We introduce Robust Privacy (RP), an inference-stage privacy notion inspired by certified robustness: if a model's prediction is provably invariant within a radius-R neighborhood around an input x with confidence at least $1-\\alpha$, then x enjoys $(R,\\alpha)$-Robust Privacy, under which we prove that any adversary observing the released prediction has at most $\\alpha/2$ advantage in distinguishing x from any input within distance R of x. Building on RP, we formalize Robust Attribute Privacy (RAP), an attribute-level privacy notion that characterizes the set of sensitive-attribute values that remain compatible with a released prediction. On a classification task, RP increases the median ","title":"Robust Privacy: Inference-Stage Privacy through Certified Robustness","url":"https://arxiv.org/abs/2601.17360","vendor":"arxiv_cs_ai"},"summary":"arXiv:2601.17360v2 Announce Type: replace-cross \nAbstract: An adversary observing a model's released prediction can infer sensitive attributes of the queried input, or even reconstruct representatives of the model's training data. The inference interface thus acts as a side channel for privacy leakage. We introduce Robust Privacy (RP), an inference-stage privacy notion inspired by certified robustness: if a model's prediction is provably invariant within a radius-R neighborhood around an input x with confidence at least $1-\\alpha$, then x enjoys $(R,\\alpha)$-Robust Privacy, under which we prove that any adversary observing the released prediction has at most $\\alpha/2$ advantage in distinguishing x from any input within distance R of x. Building on RP, we formalize Robust Attribute Privacy (RAP), an attribute-level privacy notion that characterizes the set of sensitive-attribute values that remain compatible with a released prediction. 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