比较系统:最大情况下的细化顺序及其在差分隐私中的应用

K. Chatzikokolakis, Natasha Fernandes, C. Palamidessi
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引用次数: 14

摘要

定量信息流(QIF)和差分隐私(DP)都涉及敏感信息的保护,但它们是截然不同的方法。特别是,QIF考虑成功攻击的预期概率,而DP(在其标准版本和本地版本中)是一个最大情况度量,在某种意义上,它被可能的攻击的存在所损害,无论其概率如何。比较系统是这些领域的一项基本任务:人们希望保证用系统B替换系统a是一个安全的操作,即B的隐私性不比a差。在QIF中,细化顺序提供了强有力的保证,而在DP机制中,通常是基于它们提供的ε隐私参数来比较(w.r.t.隐私)。在本文中,我们在QIF的启发下,探索了各种改进顺序,为最大情况下的泄漏提供了精确的保证。我们研究了表征它们的简单结构方法,它们之间的关系,验证它们的有效方法以及它们的晶格性质。此外,我们将这些顺序应用于比较DP机制的任务中,提出了基于ε的顺序是否提供强隐私保证的问题。我们表明,虽然同一“家族”(几何,随机响应等)的机制通常是这种情况,但它很少在不同的家族中成立。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparing Systems: Max-Case Refinement Orders and Application to Differential Privacy
Quantitative Information Flow (QIF) and Differential Privacy (DP) are both concerned with the protection of sensitive information, but they are rather different approaches. In particular, QIF considers the expected probability of a successful attack, while DP (in both its standard and local versions) is a max-case measure, in the sense that it is compromised by the existence of a possible attack, regardless of its probability. Comparing systems is a fundamental task in these areas: one wishes to guarantee that replacing a system A by a system B is a safe operation, that is the privacy of B is no-worse than that of A. In QIF, a refinement order provides strong such guarantees, while in DP mechanisms are typically compared (w.r.t. privacy) based on the ε privacy parameter that they provide. In this paper we explore a variety of refinement orders, inspired by the one of QIF, providing precise guarantees for max-case leakage. We study simple structural ways of characterising them, the relation between them, efficient methods for verifying them and their lattice properties. Moreover, we apply these orders in the task of comparing DP mechanisms, raising the question of whether the order based on ε provides strong privacy guarantees. We show that, while it is often the case for mechanisms of the same "family" (geometric, randomised response, etc.), it rarely holds across different families.
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