Privacy Preserving Frequent Itemset Mining

Oliveira, S.R.M. and Zaiane, O.R.

One crucial aspect of privacy preserving frequent itemset mining is the fact that the mining process deals with a trade-off: privacy and accuracy, which are typically contradictory, and improving one usually incurs a cost in the other. One alternative to address this particular problem is to look for a balance between hiding restrictive patterns and disclosing non-restrictive ones. In this paper, we propose a new framework for enforcing privacy in mining frequent itemsets. We combine, in a single framework, techniques for efficiently hiding restrictive patterns: a transaction retrieval engine relying on an inverted file and Boolean queries; and a set of algorithms to 'sanitize' a database. In addition, we introduce performance measures for mining frequent itemsets that quantify the fraction of mining patterns which are preserved after sanitizing a database. We also report the results of a performance evaluation of our research prototype and an analysis of the results.

Cite as: Oliveira, S.R.M. and Zaiane, O.R. (2002). Privacy Preserving Frequent Itemset Mining. In Proc. IEEE ICDM Workshop on Privacy, Security and Data Mining (PSDM 2002), Maebashi City, Japan. CRPIT, 14. Clifton, C. and Estivill-Castro, V., Eds. ACS. 43-54.

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