Efficient Apriori based algorithms for privacy preserving frequent itemset mining

Adrián Csiszárik, Szilvia Lestyán, András Lukács

Abstract: Frequent Itemset Mining as one of the principal routine of data analysis and a basic tool of large scale information aggregation also bears a serous interest in Privacy Preserving Data Mining. In this paper Apriori based distributed, privacy preserving Frequent Itemset Mining algorithms are considered. Our secure algorithms are designed to fit in the Secure Multiparty Computation model of privacy preserving computation.