Rakesh Agrawal, Johannes Gehrke, et al.
Data Mining and Knowledge Discovery
We present a framework for mining association rules from transactions consisting of categorical items where the data has been randomized to preserve privacy of individual transactions. While it is feasible to recover association rules and preserve privacy using a straightforward "uniform" randomization, the discovered rules can unfortunately be exploited to find privacy breaches. We analyze the nature of privacy breaches and propose a class of randomization operators that are much more effective than uniform randomization in limiting the breaches. We derive formulae for an unbiased support estimator and its variance, which allow us to recover itemset supports from randomized datasets, and show how to incorporate these formulae into mining algorithms. Finally, we present experimental results that validate the algorithm by applying it on real datasets. © 2003 Published by Elsevier Ltd.
Rakesh Agrawal, Johannes Gehrke, et al.
Data Mining and Knowledge Discovery
Ching-Tien Ho, Rakesh Agrawal, et al.
SIGMOD Record (ACM Special Interest Group on Management of Data)
Rakesh Agrawal, Jerry Kiernan, et al.
WWW 2003
Krishna Kummamuru, Raghu Krishnapuram, et al.
ICDM 2005