Composition of Differential Privacy & Privacy Amplification by Subsampling

@article{Steinke2022CompositionOD,
  title={Composition of Differential Privacy \& Privacy Amplification by Subsampling},
  author={Thomas Steinke},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.00597}
}
  • T. Steinke
  • Published 2 October 2022
  • Computer Science
  • ArXiv
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… 

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