@conference {1637684,
	title = {Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling.},
	booktitle = {In Foundations of Responsible Computing (FORC 2022)},
	year = {2022},
	abstract = {
	Sampling schemes are fundamental tools in statistics, survey design, and algorithm design. A fundamental result\&nbsp;in differential privacy is that a differentially private mechanism run on a simple random sample of a population\&nbsp;provides stronger privacy guarantees than the same algorithm run on the entire population. However, in practice,\&nbsp;sampling designs are often more complex than the simple, data-independent sampling schemes that are addressed\&nbsp;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\&nbsp;actually result in privacy degradation. We analyze the privacy implications of the pervasive cluster sampling and\&nbsp;stratified sampling paradigms, as well as provide some insight into the study of more general sampling designs.

},
	author = {Mark Bun and J{\"o}rg Drechsler and Marco Gaboardi and Audra McMillan and Jayshree Sarathy}
}
