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

• Published in: IEEE transactions on services computing Vol. 10; no. 4; pp. 633 - 645
• Main Authors: Ahluwalia, Madhu V., Gangopadhyay, Aryya, Zhiyuan Chen, Yesha, Yelena
• Format: Journal Article
• Language: English
• Published: IEEE 01-07-2017
• Subjects: and protection; Association rules; data mining; Data privacy; Discrete wavelet transforms; Distributed databases; integrity; mining methods and algorithms; security
• ISSN: 1939-1374; 1939-1374; 2372-0204
• DOI: 10.1109/TSC.2015.2484318

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 securely mining big data.