Privacy-preserving frequent itemsets mining via secure collaborative framework

Kok Seng Wong, Myung Ho Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Knowledge-discovering or pattern-discovering process, such as data mining, is an important technique to discover hidden but useful information from a large volume of data. Under distributed environment, data mining task has become a challenging task due to data protection and privacy concerns. The secure multi-party computation (SMC) approach has been widely used to solve privacy-preserving data mining problems. However, generic SMC solutions are not practical from an efficiency point of view, especially when the number of parties and the size of the data are large. In view of these problems, we utilize a secure collaborative framework to facilitate the computation protocol for SMC. In this paper, we particularly consider the problem of privacy-preserving frequent itemsets mining under distributed environment. Our solution reduces the risk for central data mining and improves the efficiency of the current generic SMC solutions. Furthermore, our solution is more reliable and flexible regardless of the number of parties involved.

Original languageEnglish
Pages (from-to)263-272
Number of pages10
JournalSecurity and Communication Networks
Issue number3
Publication statusPublished - Jan 1 2012
Externally publishedYes


  • Collaborative framework
  • Distributed association rules mining
  • Frequent itemsets mining
  • Multi-party computation
  • Privacy preserving

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Privacy-preserving frequent itemsets mining via secure collaborative framework'. Together they form a unique fingerprint.

Cite this