Privacy preserving spectral clustering over vertically partitioned data sets

Abstract

Spectral clustering is one of the most popular modern clustering techniques that often outperforms other clustering techniques. When data owned by different parties are used for analysis, the cooperating parties may need to perform spectral clustering jointly, even if the parties may not be willing to disclose their private data to each other. In this paper we develop privacy preserving spectral clustering protocols over vertically partitioned data sets. Such protocols allow various parties to analyze their data jointly while protecting their privacy.