"f differential privacy" (fDP) is a recent definition for privacy privacy which can offer improved predictions of "privacy loss". It has been used to analyse specific privacy mechanisms, such as the popular Gaussian mechanism. In this paper we show how fDP's foundation in statistical hypothesis testing implies equivalence to the channel model of Quantitative Information Flow. We demonstrate this equivalence by a Galois connection between two partially ordered sets. This equivalence enables novel general composition theorems for fDP, supporting improved analysis for complex privacy designs.
翻译："f差分隐私"(fDP)是近期提出的一种隐私定义，能够提供更精确的"隐私损失"预测。该定义已被用于分析特定隐私机制，例如广泛使用的高斯机制。本文通过揭示fDP在统计假设检验中的理论基础，论证其与定量信息流的信道模型具有等价性。我们通过两个偏序集之间的伽罗瓦连接来证明这一等价关系。该等价性使得我们能够建立fDP的新型通用复合定理，从而为复杂隐私设计提供更优的分析框架。
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