SoK: Privacy-preserving Clustering (Extended Abstract)

Aditya Hegde, Helen Möllering, Thomas Schneider, Hossein Yalame

Keywords: Privacy-preserving Protocols, Clustering, Secure Computation

TL;DR: The first empirical evaluation and analysis of existing techniques and protocols used for privacy-preserving clustering with respect to security models, privacy limitations, efficiency, and further aspects.

Abstract: Clustering is a popular unsupervised machine learning technique that groups similar input elements into clusters. In many applications, sensitive information is clustered that should not be leaked. Moreover, nowadays it is often required to combine data from multiple sources to increase the quality of the analysis as well as to outsource complex computation to powerful cloud servers. This calls for efficient privacy-preserving clustering. In this work, we systematically analyze the state-of-the-art in privacy-preserving clustering. We implement and benchmark today's four most efficient fully private clustering protocols by Cheon et al. (SAC'19), Meng et al. (ArXiv'19), Mohassel et al. (PETS'20), and Bozdemir et al. (ASIACCS'21) with respect to communication, computation, and clustering quality.

Paper Under Submission: The paper is NOT under submission at NeurIPS