This tutorial studies relationships between differential privacy and various information-theoretic measures by using several selective articles. In particular, we present how these connections can provide new interpretations for the privacy guarantee in systems that deploy differential privacy in an information-theoretic framework. To this end, the tutorial provides an extensive summary on the existing literature that makes use of information-theoretic measures and tools such as mutual information, min-entropy, Kullback-Leibler divergence and rate-distortion function for quantification and characterization of differential privacy in various settings.

翻译：本教程通过精选文献，系统研究了差分隐私与多种信息论度量之间的关联。具体而言，我们阐释了这些关联如何为采用信息论框架部署差分隐私的系统提供隐私保障的新诠释。为此，本教程全面梳理了现有文献中利用互信息、最小熵、Kullback-Leibler散度及率失真函数等信息论度量与工具，在不同场景下量化与表征差分隐私的研究成果。