Privacy preserving mining of association rules

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 nd privacy breaches. We analyze the nature of privacy breaches and propose a class of randomization operators that are much more e ective 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.

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Alexandre V. Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal, Johannes Gehrke
• Keywords: Data Mining, KDD 2002, Privacy Breaches, Randomization Operators, Uniform Randomization