The Test of Tests: A Framework for Differentially Private Hypothesis Testing
Abstract
We present a generic framework for creating differentially private versions of any hypothesis test in a black-box way. We analyze the resulting tests analytically and experimentally. Most crucially, we show good practical performance for small data sets, showing that at ε = 1 we only need 5-6 times as much data as in the fully public setting. We compare our work to the one existing framework of this type, as well as to several individually-designed private hypothesis tests. Our framework is higher power than other generic solutions and at least competitive with (and often better than) individually-designed tests.
Cite
Text
Kazan et al. "The Test of Tests: A Framework for Differentially Private Hypothesis Testing." International Conference on Machine Learning, 2023.Markdown
[Kazan et al. "The Test of Tests: A Framework for Differentially Private Hypothesis Testing." International Conference on Machine Learning, 2023.](https://mlanthology.org/icml/2023/kazan2023icml-test/)BibTeX
@inproceedings{kazan2023icml-test,
title = {{The Test of Tests: A Framework for Differentially Private Hypothesis Testing}},
author = {Kazan, Zeki and Shi, Kaiyan and Groce, Adam and Bray, Andrew P},
booktitle = {International Conference on Machine Learning},
year = {2023},
pages = {16131-16151},
volume = {202},
url = {https://mlanthology.org/icml/2023/kazan2023icml-test/}
}