Differentially private correlation clustering
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 $\Omega(n)$.
|Proceedings of the International Conference on Machine Learning|
Bun, M, Eliáš, M, & Kulkarni, J. (2021). Differentially private correlation clustering. In Proceedings of the International Conference on Machine Learning.