Blowfish privacy is a recent generalisation of differential privacy that enables improved utility while maintaining privacy policies with semantic guarantees, a factor that has driven the popularity of differential privacy in computer science. This paper relates Blowfish privacy to an important measure of privacy loss of information channels from the communications theory community: min-entropy leakage. Symmetry in an input data neighbouring relation is central to known connections between differential privacy and min-entropy leakage. But while differential privacy exhibits strong symmetry, Blowfish neighbouring relations correspond to arbitrary simple graphs owing to the framework's flexible privacy policies. To bound the min-entropy leakage of Blowfish-private mechanisms we organise our analysis over symmetrical partitions corresponding to orbits of graph automorphism groups. A construction meeting our bound with asymptotic equality demonstrates sharpness.

中文翻译：

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Blowfish隐私下通道信息泄漏的图对称化边界

Blowfish 隐私是差分隐私的最新概括，它可以提高效用，同时维护具有语义保证的隐私策略，这是推动差分隐私在计算机科学中流行的一个因素。本文将河豚隐私与来自通信理论界的信息渠道隐私损失的重要衡量标准联系起来：最小熵泄漏。输入数据相邻关系中的对称性是差分隐私和最小熵泄漏之间已知联系的核心。但是，虽然差分隐私表现出很强的对称性，但由于框架灵活的隐私策略，Blowfish 相邻关系对应于任意的简单图。为了限制 Blowfish 私有机制的最小熵泄漏，我们组织了对对应于图自同构群轨道的对称分区的分析。满足我们与渐近相等的界限的构造展示了锐度。