🤖 AI Summary
This work addresses the limitations of existing privacy-preserving frameworks—such as differential privacy and statistical disclosure control—in contextual adaptability and semantic rigor, which often hinder the provision of concrete and interpretable guarantees. The paper proposes a novel privacy quantification approach grounded in Bayesian decision theory, advocating for disclosure decisions based on a priori perspectives rather than data-conditional viewpoints. By introducing a context-aware and semantically precise definition of Bayesian adversarial privacy, the method transcends the rigid assumptions inherent in traditional differential privacy and establishes a more rigorous formal framework. Through adversarial modeling, prior-based inference, and concrete computational examples, both theoretical analysis and illustrative case studies demonstrate that the proposed approach offers significant advantages in semantic expressiveness and practical feasibility for privacy preservation.
📝 Abstract
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.