Secure Mining of Association Rules in Distributed Datasets

• Main Authors: Qilong Han, Dan Lu, Kejia Zhang, Hongtao Song, Haitao Zhang
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Data mining; distributed databases; association rule mining; differential privacy; privacy preserving
• Online Access: https://ieeexplore.ieee.org/document/8873600/

The arrival of Information Age, with its rapid development of information technology, has provided a wide space for Data Analysis and Mining. Yet growth in this market could be held back by privacy concerns. This paper addresses the problem of secure association rule mining where transactions are distributed across sources. The existing solutions for distributed data (vertical partition and horizontal partition) have high complexity of encryption and incomplete definition of attributes of multiple parties. In this paper, we study how to maintain differential privacy in distributed databases for mining of association rules without revealing each party's raw transactions despite how strong background knowledge the attackers have. We use a intermediate server for data consolidation without assuming it is safe. Our methods offer enhanced privacy against various attacks model. In addition, it is simpler and is significantly more efficient in terms of communication rounds and computation overhead.