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Privacy preserving itemset mining through fake transactions

This work investigates the problem of privacy-preserving mining of association rules. Specifically, a fake transaction randomization method is presented to protect the privacy of data. This method ensures the privacy of data by mixing real transactions with fake transactions. An algorithm for reconstructing frequent itemsets from the mixture of both fake transactions and real transactions is then proposed. The proposed algorithm can use any off-the-shelf tool to mine frequent itemsets without rewriting their codes, making this algorithm quite easy to implement. The performance of this algorithm is validated against several real datasets. Similar to earlier approaches, this algorithm does not always reconstruct a set of frequent itemsets that is anti-monotonic. This work uses this property to further improve the quality of the mining results.