Privacy preserving association rule mining revisited: Privacy enhancement and resources efficiency

Abedelaziz Mohaisen, Nam Su Jho, Dowon Hong, Dae Hun Nyang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Privacy preserving association rule mining algorithms have been designed for discovering the relations between variables in data while maintaining the data privacy. In this article we revise one of the recently introduced schemes for association rule mining using fake transactions (FS). In particular, our analysis shows that the FS scheme has exhaustive storage and high computation requirements for guaranteeing a reasonable level of privacy. We introduce a realistic definition of privacy that benefits from the average case privacy and motivates the study of a weakness in the structure of FS by fake transactions filtering. In order to overcome this problem, we improve the FS scheme by presenting a hybrid scheme that considers both privacy and resources as two concurrent guidelines. Analytical and empirical results show the efficiency and applicability of our proposed scheme.

Original languageEnglish
Pages (from-to)315-325
Number of pages11
JournalIEICE Transactions on Information and Systems
Issue number2
StatePublished - 2010


  • Association rule mining
  • Data sharing
  • Performance evaluation
  • Privacy preservation
  • Resources efficiency


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