Secure Multiparty Protocol for Distributed Mining of Association Rules
Keywords:
Privacy Preserving Data Mining, Frequent Item Sets, Association Rules, Secure Multiparty Protocol, Distributed Mining, Homogeneous Databases, Secure Sum ComputationAbstract
Association rule mining is one of the data mining tasks to find the association between the items or among the item sets. The privacy concept arises when the database is in the distributed environment in which each database holder is interested to discover the globally supported association rules by participating themselves in mining process without revealing its individual locally supported association rules. The present leading protocol consumes computational overhead as well as communication overhead in order to preserve the privacy in mining. A new protocol is proposed to reduce the communication cost and computational cost for secure mining in homogeneous databases. The proposed protocol uses the secure sum computation and set union computation for determining global association rules. It is significantly more secure and efficient as compared with the existing protocol.
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