Differentially Private Correlation Clustering

Mark Bun · Marek Elias · Janardhan Kulkarni

[ Abstract ] [ Livestream: Visit Privacy 1 ] [ Paper ]
Thu 22 Jul 6:40 a.m. — 6:45 a.m. PDT

Correlation clustering is a widely used technique in unsupervised machine learning. Motivated by applications where individual privacy is a concern, we initiate the study of differentially private correlation clustering. We propose an algorithm that achieves subquadratic additive error compared to the optimal cost. In contrast, straightforward adaptations of existing non-private algorithms all lead to a trivial quadratic error. Finally, we give a lower bound showing that any pure differentially private algorithm for correlation clustering requires additive error Ω(n).

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