论文标题
最大泄漏
Pointwise Maximal Leakage
论文作者
论文摘要
我们介绍了一种称为“最大泄漏”的隐私措施，概括了最大泄漏的先前概念，该概念通过披露在$ x $上计算出的（随机）功能的单个结果，从而量化了有关秘密$ x $的信息量。最大泄漏是一种强大且具有操作意义的隐私措施，它捕获了大约$ x $泄漏的最大信息，以寻求猜测$ x $的任意（可能是随机的）功能，或等效地，旨在最大程度地提高任意收益功能。我们研究了最大泄漏的几种属性，例如，它如何在多个结果上构成，如何受到处理前后的影响等。此外，我们建议将信息泄漏视为随机变量，从而使我们可以将隐私性保证视为对信息泄漏的不同统计泄漏随机变量的需求。我们定义了几种隐私保证，并研究了它们如何在预处理，后处理和组成下的行为。最后，我们研究了最大泄漏和其他隐私概念（例如本地差异隐私，本地信息隐私，$ f $ information等）之间的关系。总体而言，我们的论文为隐私风险评估构建了一个强大而灵活的框架，其中央概念具有强大的操作含义，可以适应各种应用和实际情况。
We introduce a privacy measure called pointwise maximal leakage, generalizing the pre-existing notion of maximal leakage, which quantifies the amount of information leaking about a secret $X$ by disclosing a single outcome of a (randomized) function calculated on $X$. Pointwise maximal leakage is a robust and operationally meaningful privacy measure that captures the largest amount of information leaking about $X$ to adversaries seeking to guess arbitrary (possibly randomized) functions of $X$, or equivalently, aiming to maximize arbitrary gain functions. We study several properties of pointwise maximal leakage, e.g., how it composes over multiple outcomes, how it is affected by pre- and post-processing, etc. Furthermore, we propose to view information leakage as a random variable which, in turn, allows us to regard privacy guarantees as requirements imposed on different statistical properties of the information leakage random variable. We define several privacy guarantees and study how they behave under pre-processing, post-processing and composition. Finally, we examine the relationship between pointwise maximal leakage and other privacy notions such as local differential privacy, local information privacy, $f$-information, and so on. Overall, our paper constructs a robust and flexible framework for privacy risk assessment whose central notion has a strong operational meaning which can be adapted to a variety of applications and practical scenarios.
