标题： 信息论学者对差分隐私的游览 显示英文标题

标题： An information theorist's tour of differential privacy

摘要： 自2006年提出以来，差分隐私已成为量化在发布或共享敏感数据分析时某些风险的标准方法。 其核心在于，差分隐私通过概率分布之间的差异来衡量风险，这是信息论中的一个核心主题。 一个差分隐私算法是在底层数据和分析结果之间的信道。 以这种方式来看，差分隐私所做出的保证可以理解为该信道的特性。 在本文中，我们探讨了信息理论与差分隐私的公式/应用之间的一些关键联系，为相关的信息度量提供了“操作性意义”。 显示英文摘要

摘要： Since being proposed in 2006, differential privacy has become a standard method for quantifying certain risks in publishing or sharing analyses of sensitive data. At its heart, differential privacy measures risk in terms of the differences between probability distributions, which is a central topic in information theory. A differentially private algorithm is a channel between the underlying data and the output of the analysis. Seen in this way, the guarantees made by differential privacy can be understood in terms of properties of this channel. In this article we examine a few of the key connections between information theory and the formulation/application of differential privacy, giving an ``operational significance'' for relevant information measures.