Persuasive Privacy

TL;DR

This paper introduces a Bayesian game-theoretic framework for measuring privacy, unifying existing concepts like differential privacy and enabling analysis of deterministic algorithms.

Contribution

It presents a new, rigorous framework for privacy measurement that encompasses existing standards and extends to deterministic algorithms, with novel interpretations and definitions.

Findings

Pure and probabilistic differential privacy are special cases of the framework.

The framework provides new interpretations of the post-processing inequality.

Privacy guarantees can be established for deterministic algorithms.

Abstract

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.