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Target-Based, Privacy Preserving, and Incremental Association Rule Mining

Authors: Madhu Ahluwalia, Aryya Gangopadhyay, Zhiyuan Chen, and Yelena Yesha

Journal: IEEE Transactions on Services Computing

Date: November 30, 2015

Abstract: We consider a special case in association rule mining where mining is conducted by a third party over data located at a central location that is updated from several source locations. The data at the central location is at rest while that flowing in through source locations is in motion. We impose some limitations on the source locations, so that the central target location tracks and privatizes changes and a third party mines the data incrementally. Our results show high efficiency, privacy and accuracy of rules for small to moderate updates in large volumes of data. We believe that the framework we develop is therefore applicable and valuable for mining big data

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