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 journalArticlepeer-review

8 Citations (Scopus)


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
Issue number4
Publication statusPublished - 2009

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'Security and correctness analysis on privacy-preserving k-means clustering schemes'. Together they form a unique fingerprint.

Cite this