Compressive k-means with differential privacy

In the compressive learning framework, one harshly com-presses a whole training dataset into a single vector of generalized randommoments, thesketch, from which a learning task can subsequently beperformed. We prove that this loss of information can be leveragedto design a differentially private mechanism, and study empirically theprivacy-utility tradeoff for the k-means clustering problem