A Novel Approach For Privacy Mining Of Generic Basic Association Rules

Waddey Moez 1 Pascal Poncelet 2 Sadok Ben Yahia 1
2 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Data mining can extract important knowledge from large data collections - but sometimes these collections are split among various parties. Privacy concerns may prevent the parties from directly sharing the data. The irony is that data mining results rarely violate privacy. The ob jective of data mining is to generalize across populations rather than reveal information about individuals [10]. Thus, the true problem is not data mining, but how data mining is done. This paper presents a new scalable algorithm for discover- ing closed frequent itemsets in distributed environment, us- ing commutative encryption to ensure privacy concerns. We address secure mining of association rules over horizontally partitioned data.
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Submitted on : Sunday, November 22, 2009 - 5:40:55 PM
Last modification on : Thursday, May 24, 2018 - 3:59:22 PM
Long-term archiving on : Thursday, June 17, 2010 - 9:23:01 PM

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Waddey Moez, Pascal Poncelet, Sadok Ben Yahia. A Novel Approach For Privacy Mining Of Generic Basic Association Rules. ACM First International Workshop on Privacy and Anonymity for Very Large Datasets, join with CIKM'09, France. pp.45-52. ⟨lirmm-00434320⟩

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