Privacy preserving two-party k-means clustering over vertically partitioned dataset

Zhenmin Lin, Jerzy W. Jaromczyk

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

We propose a secure approximate comparison protocol and develop a practical privacy-preserving two-party k-means clustering algorithm over vertically partitioned dataset. Experiments with to real datasets show that the accuracy of clustering achieved with our privacy preserving protocol is similar to the standard (non-secure) kmeans function in MATLAB.

Original languageEnglish
Title of host publicationProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
Pages187-191
Number of pages5
DOIs
StatePublished - 2011
Event2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011 - Beijing, China
Duration: Jul 10 2011Jul 12 2011

Publication series

NameProceedings of 2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011

Conference

Conference2011 IEEE International Conference on Intelligence and Security Informatics, ISI 2011
Country/TerritoryChina
CityBeijing
Period7/10/117/12/11

Keywords

  • k-means
  • privacy preserving
  • secure approximate comparison

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

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