Bayesian Adversarial Privacy
This work addresses the problem of defining a more explicit and rigorous notion of privacy for researchers and practitioners working with sensitive information, offering an alternative to existing frameworks.
This paper introduces a new quantitative and contextual notion of privacy based on Bayesian decision theory. It argues that this new framework offers a more meaningful and rigorous approach to privacy compared to differential privacy and statistical disclosure theory.
Theoretical and applied research into privacy encompasses an incredibly broad swathe of differing approaches, emphasis and aims. This work introduces a new quantitative notion of privacy that is both contextual and specific. We argue that it provides a more meaningful notion of privacy than the widely utilised framework of differential privacy and a more explicit and rigorous formulation than what is commonly used in statistical disclosure theory. Our definition relies on concepts inherent to standard Bayesian decision theory, while departing from it in several important respects. In particular, the party controlling the release of sensitive information should make disclosure decisions from the prior viewpoint, rather than conditional on the data, even when the data is itself observed. Illuminating toy examples and computational methods are discussed in high detail in order to highlight the specificities of the method.