Cryptology ePrint Archive: Report 2021/809

SoK: Efficient Privacy-preserving Clustering

Aditya Hegde and Helen Möllering and Thomas Schneider and Hossein Yalame

Abstract: Clustering is a popular unsupervised machine learning technique that groups similar input elements into clusters. It is used in many areas ranging from business analysis to health care. In many of these 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. We compare them, assess their limitations for a practical use in real-world applications, and conclude with open challenges.

Category / Keywords: applications / Privacy-preserving Protocols, Clustering, Secure Computation

Original Publication (in the same form): PoPETs '21

Date: received 14 Jun 2021

Contact author: moellering at encrypto cs tu-darmstadt de

Available format(s): PDF | BibTeX Citation

Version: 20210616:132944 (All versions of this report)

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