NIPS Proceedingsβ

Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences

Part of: Advances in Neural Information Processing Systems 31 (NIPS 2018)

[PDF] [BibTeX] [Supplemental] [Reviews]


Conference Event Type: Poster


Differential privacy comes equipped with multiple analytical tools for the design of private data analyses. One important tool is the so-called "privacy amplification by subsampling" principle, which ensures that a differentially private mechanism run on a random subsample of a population provides higher privacy guarantees than when run on the entire population. Several instances of this principle have been studied for different random subsampling methods, each with an ad-hoc analysis. In this paper we present a general method that recovers and improves prior analyses, yields lower bounds and derives new instances of privacy amplification by subsampling. Our method leverages a characterization of differential privacy as a divergence which emerged in the program verification community. Furthermore, it introduces new tools, including advanced joint convexity and privacy profiles, which might be of independent interest.