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Title: Differentially Private Identity and Equivalence Testing of Discrete Distributions
We study the fundamental problems of identity and equivalence testing over a discrete populationfrom random samples. Our goal is to develop efficient testers while guaranteeingdifferential privacy to the individuals of the population. We provide sample-efficient differentially private testers for these problems.Our theoretical results significantly improve over the best known algorithms for identity testing, and are the first results for private equivalence testing. The conceptual message of our work is thatthere exist private hypothesis testers that are nearly as sample-efficient as their non-private counterparts. We perform an experimental evaluation of our algorithms on synthetic data. Our experiments illustrate that our private testers achieve small type I and type II errors with sample size sublinear in the domain size of the underlying distributions.
Authors:
; ;
Award ID(s):
1740751 1733808 1741137 1650733
Publication Date:
NSF-PAR ID:
10065946
Journal Name:
Proceedings of the 35th International Conference on Machine Learning (ICML 2018)
Page Range or eLocation-ID:
169-178
Sponsoring Org:
National Science Foundation
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