document: 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 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 .
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