Scalable Differentially Private Clustering via Hierarchically Separated Trees
Scalable Differentially Private Clustering via Hierarchically Separated Trees
We study the private k-median and k-means clustering problem in d dimensional Euclidean space. By leveraging tree embeddings, we give an efficient and easy to implement algorithm, that is empirically competitive with state of the art non private methods. We prove that our method computes a solution with cost at …