Privacy preserving itemset mining through noisy items

This work investigates the problem of privacy-preserving mining of frequent itemsets. We propose a procedure to protect the privacy of data by adding noisy items to each transaction. Then, an algorithm is proposed to reconstruct frequent itemsets from these noise-added transactions. The experimental results indicate that this method can achieve a rather high level of accuracy. Our method utilizes existing algorithms for frequent itemset mining, and thereby takes full advantage of their progress to mine frequent itemset efficiently.