Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling

Abstract

"Sampling schemes are fundamental tools in statistics, survey design, and algorithm design. A fundamental result in differential privacy is that a differentially private mechanism run on a simple random sample of a population provides stronger privacy guarantees than the same algorithm run on the entire population. However, in practice, sampling designs are often more complex than the simple, data-independent sampling schemes that are addressed in prior work. In this work, we extend the study of privacy amplification results to more complex, data-dependent sampling schemes. We find that not only do these sampling schemes often fail to amplify privacy, they can actually result in privacy degradation. We analyze the privacy implications of the pervasive cluster sampling and stratified sampling paradigms, as well as provide some insight into the study of more general sampling designs." (Author's abstract, IAB-Doku) ((en))

Cite article

Bun, M., Drechsler, J., Gaboardi, M., McMillan, A. & Sarathy, J. (2022): Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling. In: L. Elisa Celis (Ed.) (2022): 3rd annual Symposium on Foundations of Responsible Computing (FORC), p. 1-24. DOI:10.4230/LIPIcs.FORC.2022.1