01939nam a2200253 a 450000100080000000500110000800800410001910000260006024500710008626001550015750000950031252009870040765300280139465300160142265300280143865300250146665300330149165300520152465300350157665300270161165300130163865300150165170000190166610085662020-01-17       2002    bl uuuu     u00u1 u    #d1 aOLIVEIRA, S. R. de M.  aPrivacy preserving frequent itemset mining.h[electronic resource]  aIn: IEEE ICDM WORKSHOP ON PRIVACY, SECURITY AND DATA MINING, 2002, Maebashi. Proceedings... Sydney: Australian Computer Society, 2002. p. 43-54.c2002  aEditors: Chris Clifton, Vladimir Estivill-Castro. Na publicação: Stanley R. M. Oliveira.  aOne 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.  aAssosiation rule mining  aData mining  aFrequent itemset mining  aMineração de dados  aPreservação de privacidade  aPrivacy preservation in association rule mining  aPrivacy preserving data mining  aRegras de associação  aSecurity  aSegurança1 aZAÏANE, O. R.