Utility-efficient differentially private K-means clustering based on cluster merging

Differential privacy is widely used in data analysis. State-of-the-art k-means clustering algorithms with differential privacy typically add an equal amount of noise to centroids for each iterative computation. In this paper, we propose a novel differentially private k-means clustering algorithm, DP-KCCM, that significantly improves the utility of clustering by adding adaptive noise and merging clusters. Specifically, to obtain k clusters with differential privacy, the algorithm first generates n x k initial centroids, adds adaptive noise for each iteration to get n x k clusters, and finally merges these clusters into k ones. We theoretically prove the differential privacy of the proposed algorithm. Surprisingly, extensive experimental results show that: 1) cluster merging with equal amounts of noise improves the utility somewhat; 2) while adding adaptive noise only does not improve the utility, combining both cluster merging and adaptive noise further improves the utility significantly. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The paper introduces a novel differentially private k-means clustering algorithm, DP-KCCM, which improves the utility of clustering significantly by adding adaptive noise and merging clusters. The algorithm first generates initial centroids, adds adaptive noise, and further improves the utility by merging clusters.