SoK: Efficient Privacy-preserving Clustering
Authors: Aditya Hegde (IIIT-Bangalore), Helen Möllering (Technical University of Darmstadt), Thomas Schneider (Technical University of Darmstadt), Hossein Yalame (Technical University of Darmstadt)
Volume: 2021
Issue: 4
Pages: 225–248
DOI: https://doi.org/10.2478/popets-2021-0068
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.
Keywords: Privacy-preserving Protocols, Clustering, Secure Computation
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