Differentially Private Maximal Information Coefficients

John Lazarsfeld · Aaron Johnson · Emmanuel Adeniran

Hall E #1004

Keywords: [ T: Everything Else ] [ APP: Genetics, Cell Biology, etc ] [ T: Social Aspects ] [ SA: Privacy-preserving Statistics and Machine Learning ]

[ Abstract ]
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Wed 20 Jul 3:30 p.m. PDT — 5:30 p.m. PDT
Spotlight presentation: SA: Trustworthy Machine Learning
Wed 20 Jul 10:15 a.m. PDT — 11:45 a.m. PDT


The Maximal Information Coefficient (MIC) is a powerful statistic to identify dependencies between variables. However, it may be applied to sensitive data, and publishing it could leak private information. As a solution, we present algorithms to approximate MIC in a way that provides differential privacy. We show that the natural application of the classic Laplace mechanism yields insufficient accuracy. We therefore introduce the MICr statistic, which is a new MIC approximation that is more compatible with differential privacy. We prove MICr is a consistent estimator for MIC, and we provide two differentially private versions of it. We perform experiments on a variety of real and synthetic datasets. The results show that the private MICr statistics significantly outperform direct application of the Laplace mechanism. Moreover, experiments on real-world datasets show accuracy that is usable when the sample size is at least moderately large.

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