1. bookVolume 2021 (2021): Issue 4 (October 2021)
Journal Details
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16 Apr 2015
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4 times per year
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access type Open Access

SoK: Efficient Privacy-preserving Clustering

Published Online: 23 Jul 2021
Page range: 225 - 248
Received: 28 Feb 2021
Accepted: 16 Jun 2021
Journal Details
License
Format
Journal
First Published
16 Apr 2015
Publication timeframe
4 times per year
Languages
English
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

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