A NOVEL PRIVACY-PRESERVING ASSOCIATION RULES MINING METHOD

作者:Tian Hong*; Wang Xiukun
来源:International Journal of Pattern Recognition and Artificial Intelligence, 2010, 24(6): 995-1009.
DOI:10.1142/S021800141000824X

摘要

In order to mine association rules accurately and efficiently while preserving the privacy thereof, a novel privacy-preserving association rules mining method is proposed in this paper. Known as the partial randomized response based on probability matrix, or PRRPM, this method chooses different data transition strategies to find frequent 1-itemsets and k-itemsets (k > 1). The PRRPM algorithm is explored and its validity examined through theoretical analysis and experiments.

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