Security and correctness analysis on privacy-preserving k-means clustering schemes

Chunhua Su, Feng Bao, Jianying Zhou, Tsuyoshi Takagi, Kouichi Sakurai

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1246-1250
Number of pages5
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE92-A
Issue number4
DOIs
Publication statusPublished - Jan 1 2009

Fingerprint

Privacy Preserving
K-means Clustering
Data mining
Correctness
Internet
Network protocols
Privacy
Data Mining
Fault
Output

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

Security and correctness analysis on privacy-preserving k-means clustering schemes. / Su, Chunhua; Bao, Feng; Zhou, Jianying; Takagi, Tsuyoshi; Sakurai, Kouichi.

In: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. E92-A, No. 4, 01.01.2009, p. 1246-1250.

Research output: Contribution to journalArticle

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