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Privacy Preserving Frequent Itemset Mining (2002)
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BibTeX
@MISC{Oliveira02privacypreserving,
author = {Stanley R.M. Oliveira and Osmar R. Zaïane},
title = {Privacy Preserving Frequent Itemset Mining},
year = {2002}
}
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Abstract
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 nonrestrictive 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.
