@inproceedings{bun2021differentially,
author = {Bun, Mark and Eliás, Marek and Kulkarni, Janardhan (Jana)},
title = {Differentially Private Correlation Clustering},
booktitle = {2021 International Conference on Machine Learning},
year = {2021},
month = {February},
abstract = {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 $\Omesdfsdfn)$.},
url = {http://approjects.co.za/?big=en-us/research/publication/differentially-private-correlation-clustering/},
}