Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [25 references]  Display  Hide  Download
Contributor : Pascal Poncelet <>
Submitted on : Sunday, November 22, 2009 - 5:40:55 PM
Last modification on : Thursday, June 3, 2021 - 3:32:11 PM
Long-term archiving on: : Thursday, June 17, 2010 - 9:23:01 PM


Publisher files allowed on an open archive


  • HAL Id : lirmm-00434320, version 1



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⟩



Record views


Files downloads