Privacy-preserving clustering with distributed EM mixture modeling

Xiaodong Lin, Chris Clifton, Michael Zhu

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


Privacy and security considerations can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery can alleviate this problem. We present a technique that uses EM mixture modeling to perform clustering on distributed data. This method controls data sharing, preventing disclosure of individual data items or any results that can be traced to an individual site.

Original languageEnglish (US)
Pages (from-to)68-81
Number of pages14
JournalKnowledge and Information Systems
Issue number1
StatePublished - Jul 2005
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence


  • Clustering
  • Privacy
  • Security


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