A Novel Approach For Privacy Mining Of Generic Basic Association Rules - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Conference Papers Year : 2009

A Novel Approach For Privacy Mining Of Generic Basic Association Rules

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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Dates and versions

lirmm-00434320 , version 1 (22-11-2009)

Identifiers

  • HAL Id : lirmm-00434320 , version 1

Cite

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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