A novel approach for privacy mining of generic basic association rules

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 objective 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 discovering closed frequent itemsets in distributed environment, using commutative encryption to ensure privacy concerns. We address secure mining of association rules over horizontally partitioned data. Categories and Subject Descriptors H.4 [Information Systems Applications]: General; H.2.8 [Database Management]: Database applications—privacy preserving distributed data mining General Terms Algorithms Security Keywords Data mining, Association rules mining, Privacy preserving...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Moez Waddey, Pascal Poncelet, Sadok Ben Yahia
• Keywords: CIKM 2009, Data Mining, Keywords Data Mining, Privacy Concerns