Differentially Private Approximate Quantiles
Machine Learning
2021-10-12 v1 Cryptography and Security
Data Structures and Algorithms
Abstract
In this work we study the problem of differentially private (DP) quantiles, in which given dataset and quantiles , we want to output quantile estimations which are as close as possible to the true quantiles and preserve DP. We describe a simple recursive DP algorithm, which we call ApproximateQuantiles (AQ), for this task. We give a worst case upper bound on its error, and show that its error is much lower than of previous implementations on several different datasets. Furthermore, it gets this low error while running time two orders of magnitude faster that the best previous implementation.
Cite
@article{arxiv.2110.05429,
title = {Differentially Private Approximate Quantiles},
author = {Haim Kaplan and Shachar Schnapp and Uri Stemmer},
journal= {arXiv preprint arXiv:2110.05429},
year = {2021}
}