Private testing of distributions via sample permutations
Name
NeurIPS-2019-private-testing-of-distributions-via-sample-permutations-Paper.pdf
Description
Published version
Size
150.53 KB
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Unknown
Checksum (MD5)
d58132abc58f6e851523e943147a96ed
Author(s)
Aliakbarpour, Maryam
Diakonikolas, Ilias
Kane, Daniel
Rubinfeld, Ronitt
Date Issued
December 2019
Journal
Advances in Neural Information Processing Systems
Citation
2019. "Private testing of distributions via sample permutations." Advances in Neural Information Processing Systems, 32.
Version
Final published version
Abstract
© 2019 Neural information processing systems foundation. All rights reserved. Statistical tests are at the heart of many scientific tasks. To validate their hypotheses, researchers in medical and social sciences use individuals' data. The sensitivity of participants' data requires the design of statistical tests that ensure the privacy of the individuals in the most efficient way. In this paper, we use the framework of property testing to design algorithms to test the properties of the distribution that the data is drawn from with respect to differential privacy. In particular, we investigate testing two fundamental properties of distributions: (1) testing the equivalence of two distributions when we have unequal numbers of samples from the two distributions. (2) Testing independence of two random variables. In both cases, we show that our testers achieve near optimal sample complexity (up to logarithmic factors). Moreover, our dependence on the privacy parameter is an additive term, which indicates that differential privacy can be obtained in most regimes of parameters for free.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://papers.nips.cc/paper/2019/hash/8e036cc193d0af59aa9b22821248292b-Abstract.html
