Privacy-preserving two-party K-means clustering via secure approximation

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

K-means clustering is a powerful and frequently used technique in data mining. However, privacy breaching is a serious problem if the k-means clustering is used without any security treatment, while privacy is a real concern in many practical applications. Recently, four privacypreserving solutions based on cryptography have been proposed by different researchers. Unfortunately none of these four schemes can achieve both security and completeness with good efficiency. In this paper, we present a new scheme to overcome the problems occurred previously. Our scheme deals with data standardization in order to make the result more reasonable. We show that our scheme is secure and complete with good efficiency.

Original languageEnglish
Title of host publicationProceedings - 21st International Conference on Advanced Information Networking and ApplicationsWorkshops/Symposia, AINAW'07
Pages385-391
Number of pages7
DOIs
Publication statusPublished - Oct 18 2007
Event21st International Conference on Advanced Information Networking and ApplicationsWorkshops/Symposia, AINAW'07 - Niagara Falls, ON, Canada
Duration: May 21 2007May 23 2007

Publication series

NameProceedings - 21st International Conference on Advanced Information Networking and Applications Workshops/Symposia, AINAW'07
Volume2

Other

Other21st International Conference on Advanced Information Networking and ApplicationsWorkshops/Symposia, AINAW'07
CountryCanada
CityNiagara Falls, ON
Period5/21/075/23/07

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software
  • Mathematics(all)

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