Home Page

Papers

Submissions

News

Editorial Board

Special Issues

Open Source Software

Proceedings (PMLR)

Data (DMLR)

Transactions (TMLR)

Search

Statistics

Login

Frequently Asked Questions

Contact Us



RSS Feed

Locally Private k-Means Clustering

Uri Stemmer; 22(176):1−30, 2021.

Abstract

We design a new algorithm for the Euclidean $k$-means problem that operates in the local model of differential privacy. Unlike in the non-private literature, differentially private algorithms for the $k$-means objective incur both additive and multiplicative errors. Our algorithm significantly reduces the additive error while keeping the multiplicative error the same as in previous state-of-the-art results. Specifically, on a database of size $n$, our algorithm guarantees $O(1)$ multiplicative error and $\approx n^{1/2+a}$ additive error for an arbitrarily small constant $a>0$. All previous algorithms in the local model had additive error $\approx n^{2/3+a}$. Our techniques extend to $k$-median clustering. We show that the additive error we obtain is almost optimal in terms of its dependency on the database size $n$. Specifically, we give a simple lower bound showing that every locally-private algorithm for the $k$-means objective must have additive error at least $\approx\sqrt{n}$.

[abs][pdf][bib]       
© JMLR 2021. (edit, beta)

Mastodon