Private k-Means Clustering with Stability Assumptions

Moshe Shechner, Or Sheffet, Uri Stemmer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We study the problem of differentially private clustering under input-stability assumptions. Despite the ever-growing volume of works on differential privacy in general and differentially private clustering in particular, only three works (Nissim et al., 2007; Wang et al., 2015; Huang and Liu, 2018) looked at the problem of privately clustering "nice" k-means instances, all three relying on the sample-and-aggregate framework and all three measuring utility in terms of Wasserstein distance between the true cluster centers and the centers returned by the private algorithm. In this work we improve upon this line of works on multiple axes. We present a simpler algorithm for clustering stable inputs (not relying on the sample-and-aggregate framework), and analyze its utility in both the Wasserstein distance and the k-means cost. Moreover, our algorithm has straight-forward analogues for "nice" k-median instances and for the local-model of differential privacy.
Original languageEnglish
Title of host publicationProceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics
EditorsSilvia Chiappa, Roberto Calandra
Number of pages11
StatePublished - 26 Aug 2020

Publication series

NameProceedings of Machine Learning Research


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