This chapter is meant to be part of the book "Differential Privacy for Artificial Intelligence Applications." We give an introduction to the most important property of differential privacy -- composition: running multiple independent analyses on the data of a set of people will still be differentially private as long as each of the analyses is private on its own -- as well as the related topic of privacy amplification by subsampling. This chapter introduces the basic concepts and gives proofs of the key results needed to apply these tools in practice.

翻译：本章意在成为《人工智能应用的不同隐私》一书的一部分。我们介绍了不同隐私的最重要属性 -- -- 构成:对一组人的数据进行多种独立的分析,只要每项分析都是私下的,就仍然具有差异性,以及相关的通过子抽样扩大隐私专题。本章介绍基本概念,并证明实际应用这些工具所需的关键结果。