Privacy Preserving EM-Based Clustering

Abstract

The problem of privacy-preserving EM-based clustering was solved when the dataset is horizontally partitioned into more than two parts (i.g., more than two computation parties). The aim of this work is to develop a method for the more difficult problem when the dataset is horizontally partitioned into only two parts. The key question is how to compute and reveal only the covariance matrix at various steps of the EM iterative process to the participating parties. We propose a method consisting of several protocols that provide privacy preservation for the computation of covariance matrices and final results without revealing the private information and the means. We also extend the proposed method for a better solution to the problem of privacy preserving k-means clustering.