论文标题
通过定量信息流的镜头在当地差异隐私中解释Epsilon
Explaining epsilon in local differential privacy through the lens of quantitative information flow
论文作者
论文摘要
对隐私泄漏措施的研究一直是一项深入研究的主题，是了解如何在计算机系统中发生隐私泄漏的重要方面。几年来，差异隐私一直是隐私社区中的焦点，但其泄漏特征尚未完全理解。在本文中，我们汇集了两个研究领域 - 信息理论和定量信息流的G-leakage框架（QIF） - 为当地差异隐私的Epsilon参数提供了操作解释。我们发现，在这两个框架中，Epsilon都是能力度量的。通过（log）lift，信息理论中的流行措施；并通过Max-Case G-Leakage，我们引入来描述任何系统泄漏到使用QIF框架下使用``最糟糕的案例''假设建模的贝叶斯对手的泄漏。我们的表征解决了伊普西隆的可解释性的重要问题，并巩固了许多不同的结果，涵盖了信息理论和定量信息流的文献。
The study of leakage measures for privacy has been a subject of intensive research and is an important aspect of understanding how privacy leaks occur in computer systems. Differential privacy has been a focal point in the privacy community for some years and yet its leakage characteristics are not completely understood. In this paper we bring together two areas of research -- information theory and the g-leakage framework of quantitative information flow (QIF) -- to give an operational interpretation for the epsilon parameter of local differential privacy. We find that epsilon emerges as a capacity measure in both frameworks; via (log)-lift, a popular measure in information theory; and via max-case g-leakage, which we introduce to describe the leakage of any system to Bayesian adversaries modelled using ``worst-case'' assumptions under the QIF framework. Our characterisation resolves an important question of interpretability of epsilon and consolidates a number of disparate results covering the literature of both information theory and quantitative information flow.
