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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.
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Antti Koskela, Joonas J“”alk“”o and Antti Honkela · 2020
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Salil Vadhan and Tianhao Wang · 2021
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