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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