Bayesian Adversarial Privacy
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...
| Authors | Cameron Bell, Timothy Johnston, Antoine Luciano et al. |
|---|---|
| Primary category | math.ST |
| Published | 4 March 2026 |
| Last updated at source | 8 September 2026 |
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