Secure Two-Party Frequent Itemset Mining With Guaranteeing Differential Privacy

Abstract

Frequent itemset mining is an essential task in data analysis. Therefore, it is crucial to design practical methods for privacy-preserving frequent itemset mining, enabling private data analysis. For two-party data analysis tasks, each party possesses its portion of the data and is reluctant to share the data with the other. While secure computation can enable two-party frequent itemset mining, the output of exact top- $k$ itemsets may still leave the adversary a chance to infer the sensitive information. Differential privacy has been utilized in various data analysis tasks to safeguard participating individuals. However, addressing how to ensure differential privacy for two-party frequent itemset mining remains unexplored. To prevent each party’s data from being leaked to the other while achieving differential privacy for releasing the output, this paper investigates the problem of differentially private two-party frequent itemset mining. We first propose a practical method that can efficiently select the frequent items of the union of two confidential databases in a differentially private way without the need to combine all elements. Then we extend this technique for general frequent itemset mining. Extensive experiments were conducted on real-world datasets, and the results show that the proposed method can achieve satisfactory utility with affordable overheads.