Differentially private k-means with constant multiplicative error

Research output: Contribution to journalConference articlepeer-review

Abstract

We design new differentially private algorithms for the Euclidean k-means problem, both in the centralized model and in the local model of differential privacy. In both models, our algorithms achieve significantly improved error guarantees than the previous state-of-the-art. In addition, in the local model, our algorithm significantly reduces the number of interaction rounds. Although the problem has been widely studied in the context of differential privacy, all of the existing constructions achieve only super constant approximation factors. We present-for the first time-efficient private algorithms for the problem with constant multiplicative error. Furthermore, we show how to modify our algorithms so they compute private coresets for k-means clustering in both models.

Original languageEnglish
Pages (from-to)5431-5441
Number of pages11
JournalAdvances in Neural Information Processing Systems
Volume2018-December
StatePublished - 2018
Event32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
Duration: 2 Dec 20188 Dec 2018

Cite this