Composition of Differential Privacy & Privacy Amplification by Subsampling. (arXiv:2210.00597v3 [cs.CR] UPDATED)

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 …

amplification differential privacy privacy