Private k-Means Clustering with Stability Assumptions

Moshe Shechner, Or Sheffet, Uri Stemmer

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


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 straightforward analogues for “nice” k-median instances and for the local-model of differential privacy.

Original languageEnglish
Pages (from-to)2518-2528
Number of pages11
JournalProceedings of Machine Learning Research
StatePublished - 2020
Event23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020 - Virtual, Online
Duration: 26 Aug 202028 Aug 2020

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