Composition of Differential Privacy & Privacy Amplification by Subsampling

• URL: http://arxiv.org/abs/2210.00597v2
• Date: Tue, 4 Oct 2022 21:41:58 GMT
• Title: Composition of Differential Privacy & Privacy Amplification by Subsampling
• Authors: Thomas Steinke
• Abstract summary: 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. This chapter introduces the basic concepts and gives proofs of the key results needed to apply these tools in practice.
• Score: 18.397305289651907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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