Secure set intersection cardinality with application to association rule mining

Jaideep Vaidya, Chris Clifton

Research output: Contribution to journalArticlepeer-review

149 Scopus citations


There has been concern over the apparent conflict between privacy and data mining. There is no inherent conflict, as most types of data mining produce summary results that do not reveal information about individuals. The process of data mining may use private data, leading to the potential for privacy breaches. Secure Multiparty Computation shows that results can be produced without revealing the data used to generate them. The problem is that general techniques for secure multiparty computation do not scale to data-mining size computations. This paper presents an efficient protocol for securely determining the size of set intersection, and shows how this can be used to generate association rules where multiple parties have different (and private) information about the same set of individuals.

Original languageEnglish (US)
Pages (from-to)593-622
Number of pages30
JournalJournal of Computer Security
Issue number4
StatePublished - 2005
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Safety, Risk, Reliability and Quality
  • Hardware and Architecture
  • Computer Networks and Communications


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