Abstract
Frequent itemset mining has been a focused theme in the field of data mining, which is widely used in business decision making, economics, medicine, bioinformatics and other fields. Frequent itemset mining can provide a lot of valuable information when making decisions, but it may bring the risk of privacy disclosure when mining and publishing frequent itemsets. In order to solve the privacy leakage problem, most of the existing solutions are using horizontal mining method to mine frequent itemsets under differential privacy. However, these solutions generally suffer from complex support computation and poor accuracy due to large candidate sets. In this paper, we propose a new vertical frequent itemset mining algorithm based on differential privacy, which is referred to as DP-Eclat. In DP-Eclat, a new privacy budget allocation strategy is proposed to rationalize the privacy budget allocation, which allows privacy budget to be used more fully. In addition, we devise a multiple pruning strategy to further improve the data utility by prune before and after the generation of candidate itemsets. Through privacy analysis, we prove that DP-Eclat satisfies E -differential privacy. Extensive experiment results on multiple real datasets show that DP-Eclat significantly outperforms state-of-the-art algorithms in terms of data utility.
Published Version
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