Differentially Private Identity and Closeness Testing of Discrete Distributions

Maryam Aliakbarpour; Ilias Diakonikolas; Ronitt Rubinfeld

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arXiv · 1707.05497

Differentially Private Identity and Closeness Testing of Discrete Distributions

Maryam Aliakbarpour·Ilias Diakonikolas·Ronitt Rubinfeld

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Abstract

We investigate the problems of identity and closeness testing over a discrete population from random samples. Our goal is to develop efficient testers while guaranteeing Differential Privacy to the individuals of the population. We describe an approach that yields sample-efficient differentially private testers for these problems. Our theoretical results show that there exist private identity and closeness 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.

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Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld. 2017-07-18. Differentially Private Identity and Closeness Testing of Discrete Distributions. https://arxiv.org/abs/1707.05497

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