Equally contributory privacy-preserving k-means clustering over vertically partitioned data

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Yi, Xun and Zhang, Yanchun (2012) Equally contributory privacy-preserving k-means clustering over vertically partitioned data. Information systems, 38 (1). pp. 97-107. ISSN 0306-4379 (print), 1873-6076 (online)

Abstract

In recent years, there have been numerous attempts to extend the k-means clustering protocol for single database to a distributed multiple database setting and meanwhile keep privacy of each data site. Current solutions for (whether two or more) multiparty k-means clustering, built on one or more secure two-party computation algorithms, are not equally contributory, in other words, each party does not equally contribute to k-means clustering. This may lead a perfidious attack where a party who learns the outcome prior to other parties tells a lie of the outcome to other parties. In this paper, we present an equally contributory multiparty k-means clustering protocol for vertically partitioned data, in which each party equally contributes to k-means clustering. Our protocol is built on ElGamal's encryption scheme, Jakobsson and Juels's plaintext equivalence test protocol, and mix networks, and protects privacy in terms that each iteration of k-means clustering can be performed without revealing the intermediate values.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/22139
DOI https://doi.org/10.1016/j.is.2012.06.001
Official URL http://www.sciencedirect.com/science/article/pii/S...
Subjects Historical > FOR Classification > 0804 Data Format
Current > Division/Research > College of Science and Engineering
Keywords ResPubID26654, privacy-preserving distributed data mining, k-means clustering, data security, cluster analysis, clusters, algorithms
Citations in Scopus 35 - View on Scopus
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