Composition of Differential Privacy Privacy Amplification by Subsampling
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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. This chapter introduces the basic concepts and gives proofs of the key results needed to apply these tools in practice.
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