Persuasive Privacy
For privacy researchers and practitioners, this provides a unified theoretical foundation that can generate new privacy definitions and assess existing ones, though it is primarily a theoretical contribution.
The paper introduces a Bayesian game-theoretic framework for privacy that generalizes differential privacy, enabling purpose-driven definitions and new interpretations of existing guarantees, while also proving privacy for deterministic algorithms.
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 this setting. Further, we demonstrate that privacy guarantees can be established for deterministic algorithms, which are overlooked by current privacy standards.