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Composition of Differential Privacy & Privacy Amplification by Subsampling. (arXiv:2210.00597v3 [cs.CR] UPDATED)
Oct. 26, 2022, 1:24 a.m. | Thomas Steinke
cs.CR updates on arXiv.org arxiv.org
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 …
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