Differentially Private Frequent Itemset Mining via Transaction Splitting

Differentially Private Frequent Itemset Mining via Transaction Splitting

Abstract— Differentially Private Frequent Itemset Mining via Transaction Splitting. A growing interest in designing differentially private data mining algorithms. Frequent item set mining < Final Year Projects 2016 > FIM is one of the most fundamental problems in data mining. In this paper, we explore the possibility of designing a differentially private FIM algorithm which can not only achieve high data utility and a high degree of privacy, but also offer high time efficiency. To this end, we propose a differentially private FIM algorithm based on the FP-growth algorithm, which is referred to as PFP-growth. The PFP-growth algorithm consists of a preprocessing phase and a mining phase. In the preprocessing phase, to improve the utility and privacy tradeoff, a novel smart splitting method is proposed to transform the database.