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Security and Correctness Analysis on Privacy-Preserving k-Means Clustering Schemes
Journal article   Peer reviewed

Security and Correctness Analysis on Privacy-Preserving k-Means Clustering Schemes

Chunhua Su, Feng Bao, Jianying Zhou, Tsuyoshi Takagi and Kouichi Sakurai
IEICE transactions on fundamentals of electronics, communications and computer sciences, Vol.E92A(4), pp.1246-1250
2009

Abstract

Computer Science Computer Science, Hardware & Architecture Computer Science, Information Systems Engineering Engineering, Electrical & Electronic Science & Technology Technology
Due to the fast development of Internet and the related IT technologies, it becomes more and more easier to access a large amount of data. k-means clustering is a powerful and frequently used technique in data mining. Many research papers about privacy-preserving k-means clustering were published. In this paper, we analyze the existing privacy-preserving k-means clustering schemes based on the cryptographic techniques. We show those schemes will cause the privacy breach and cannot output the correct results due to the faults in the protocol construction. Furthermore, we analyze our proposal as an option to improve such problems but with intermediate information breach during the computation.

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