Differentially private correlation clustering
Bun, Mark; Elias, Marek; Kulkarni, Janardhan
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 of Ω (n).
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