  Correlation clustering is a central problem in unsupervised learning, with
applications spanning community detection, duplicate detection, automated
labelling and many more. In the correlation clustering problem one receives as
input a set of nodes and for each node a list of co-clustering preferences, and
the goal is to output a clustering that minimizes the disagreement with the
specified nodes' preferences. In this paper, we introduce a simple and
computationally efficient algorithm for the correlation clustering problem with
provable privacy guarantees. Our approximation guarantees are stronger than
those shown in prior work and are optimal up to logarithmic factors.
