Collaboratively Mining Maximal Frequent Pattern Relations WITHOUT Disclosing Private Data

R. Hatakeyama, J. Wang, E. Kodama, and T. Takata (Japan)


Global FP-tree, Global Patterns, Maximal Pattern RelationMining, Distributed Computing, Privacy-Preserving


In the modern business world, very often two parties collaborate with each other for their mutual benefit. Accordingly, transactions are processed between them, with one party processing a part of a transaction, and the other continuing with the remainder. Because of the mutual advantage it brings to the collaborators, to discover such a pattern relation becomes especially important that, a frequent pattern of one party is dependent upon, or associated with, a frequent pattern of the other party. Generally it is required that pattern relation mining should be conducted without disclosing private data to each other. And also, since any subpattern of a frequent pattern is also frequent, it is sufficient to mine only the maximal frequent patterns. In this paper we propose an effective privacy-preserving maximal pattern relation mining algorithm, called CMPRM.

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