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This paper proposes a novel Bayesian framework for privacy that models disclosure decisions in adversarial settings, distinguishing itself by enabling meaningful inference about the underlying data rather than just conditional probabilities. By shifting the decision-making perspective to the prior viewpoint, it offers a rigorous theoretical extension of differential privacy and statistical disclosure control specifically relevant to machine learning security.
Bayesian Adversarial Privacy
arXiv:2603.04199v2 Announce Type: replace-cross Abstract: Theoretical and applied research into privacy encompasses an incredibly broad swathe of differing approaches, emphases and aims. This work introduces a novel quantitative notion of privacy that is both contextual and specific. Building on and extending ideas from statistical disclosure control and differential privacy, our aim is to model the implications of a disclosure decision in an adversarial setting. Our definition relies on concepts inherent to standard Bayesian decision theory, while departing from them in several important respects. In particular, (i) inference about the data itself becomes meaningful and (ii) the party controlling the release of sensitive information should make disclosure decisions from the prior viewpoint, rather than conditional on the data, which is a feature shared with Bayesian design. Illuminating toy examples are exploited towards highlighting the specificities of the method.
Abstract:Theoretical and applied research into privacy encompasses an incredibly broad swathe of differing approaches, emphases and aims. This work introduces a novel quantitative notion of privacy that is both contextual and specific. Building on and extending ideas from statistical disclosure control and differential privacy, our aim is to model the implications of a disclosure decision in an adversarial setting. Our definition relies on concepts inherent to standard Bayesian decision theory, while departing from them in several important respects. In particular, (i) inference about the data itself becomes meaningful and (ii) the party controlling the release of sensitive information should make disclosure decisions from the prior viewpoint, rather than conditional on the data, which is a feature shared with Bayesian design. Illuminating toy examples are exploited towards highlighting the specificities of the method.
Statistics Theory (math.ST); Cryptography and Security (cs.CR); Machine Learning (cs.LG); Methodology (stat.ME)
arXiv:2603.04199 [math.ST]
(or arXiv:2603.04199v2 [math.ST] for this version)
https://doi.org/10.48550/arXiv.2603.04199 Focus to learn more arXiv-issued DOI via DataCite
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