Permute-and-Flip: A new mechanism for differentially private selection
This work addresses the need for more efficient private data selection mechanisms, offering incremental improvements over existing methods.
The paper tackles the problem of differentially private selection by proposing a new mechanism called Permute-and-Flip, which always achieves an expected score at least as high as the exponential mechanism and can improve it by up to a factor of two.
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.