Persuasive Privacy

We propose a novel framework for measuring privacy from a Bayesian game-theoretic perspective. This framework enables the creation of new, purpose-driven privacy definitions that are rigorously justified, while also allowing for the assessment of existing privacy guarantees through game theory. We show that pure and probabilistic differential privacy are special cases of our framework, and provide new interpretations of the post-processing inequality in these settings. Further, we demonstrate that privacy guarantees can be established for deterministic algorithms, which are overlooked by current privacy standards.

Many organisations routinely release data summaries based on sensitive information. This paper introduces a new way to define and measure the privacy of such a release by modelling it as a strategic game between a data holder and an adversary. The approach is flexible, allowing privacy definitions to be tailored to specific real-world contexts, and also recovers several existing privacy measures. Notably, the framework can certify privacy for deterministic algorithms and statistics (those with no randomness) which existing standards largely fail to address.