We consider the problem of differentially private selection. Given a finite set of candidate items and a quality score for each item, our goal is to design a differentially private mechanism that returns an item with a score that is as high as possible. The most commonly used mechanism for this task is the exponential mechanism. In this work, we propose a new mechanism for this task based on a careful analysis of the privacy constraints. The expected score of our mechanism is always at least as large as the exponential mechanism, and can offer improvements up to a factor of two. Our mechanism is simple to implement and runs in linear time.
@article{arxiv.2010.12603,
title = {Permute-and-Flip: A new mechanism for differentially private selection},
author = {Ryan McKenna and Daniel Sheldon},
journal= {arXiv preprint arXiv:2010.12603},
year = {2020}
}