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.

翻译：我们提出了一种从贝叶斯博弈论视角衡量隐私的新框架。该框架能够创建经过严格论证的、目标驱动的新型隐私定义，同时允许通过博弈论评估现有隐私保障。我们证明纯差分隐私与概率差分隐私均是我们框架的特例，并在此背景下对后处理不等式提供了新的解释。此外，我们论证了确定性算法同样可以建立隐私保障，而现有隐私标准往往忽视了这类算法。