Differentially Private Clustering via Maximum Coverage

Matthew Jones; Huy Lê Nguyen; Thy Nguyen

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arXiv · 2008.12388

Differentially Private Clustering via Maximum Coverage

Matthew Jones·Huy Lê Nguyen·Thy Nguyen

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Abstract

This paper studies the problem of clustering in metric spaces while preserving the privacy of individual data. Specifically, we examine differentially private variants of the k-medians and Euclidean k-means problems. We present polynomial algorithms with constant multiplicative error and lower additive error than the previous state-of-the-art for each problem. Additionally, our algorithms use a clustering algorithm without differential privacy as a black-box. This allows practitioners to control the trade-off between runtime and approximation factor by choosing a suitable clustering algorithm to use.

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Matthew Jones, Huy Lê Nguyen, Thy Nguyen. 2020-08-27. Differentially Private Clustering via Maximum Coverage. https://arxiv.org/abs/2008.12388

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