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arXiv · 2210.00597

Composition of Differential Privacy & Privacy Amplification by Subsampling

Thomas Steinke
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Abstract

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.

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Thomas Steinke. 2022-10-02. Composition of Differential Privacy & Privacy Amplification by Subsampling. https://arxiv.org/abs/2210.00597

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