Collusion-Resistant protocol for privacy-preserving distributed association rules mining

Authors:
Xin-Jing Ge;Jian-Ming Zhu
Affiliations:
School of Information, Central University of Finance and Economics, Beijing, P.R. China;School of Information, Central University of Finance and Economics, Beijing, P.R. China
Venue:
ICICS'09 Proceedings of the 11th international conference on Information and Communications Security
Year:
2009

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Abstract

Privacy-preserving data mining (PPDM) primarily addresses the incorporation of privacy preserving concerns to data mining techniques. In this paper, we explore the problem of privacy-preserving distributed association rule mining in vertically partitioned data among multiple parties, and propose a collusion-resistant protocol of distributed association rules mining based on the threshold homomorphic encryption scheme, which can prevent effectively the collusion behaviors and conduct the computations across the parties without compromising their data privacy. In addition, the correctness, complexity and security of the collusion-resistant protocol are analyzed, and the result shows that the protocol has a reasonable efficiency and security.