论文标题
通过亚采样组成差异隐私和隐私放大
Composition of Differential Privacy & Privacy Amplification by Subsampling
论文作者
论文摘要
本章旨在成为“人工智能应用的差异隐私”一书的一部分。我们介绍了差异隐私的最重要属性 - 构图：只要每个分析本身都是私人的，以及对一组人的数据进行多次独立分析，以及通过亚采样的相关主题。本章介绍了基本概念，并证明了在实践中应用这些工具所需的关键结果。
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.
