Hiding Predictive Association Rules on Horizontally Distributed Data

Authors:
Shyue-Liang Wang;Ting-Zheng Lai;Tzung-Pei Hong;Yu-Lung Wu
Affiliations:
Department of Information Management,;Institute of Information Management, I-Shou University, Kaohsiung, Taiwan 84001;Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan 81148;Institute of Information Management, I-Shou University, Kaohsiung, Taiwan 84001
Venue:
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Year:
2009

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Abstract

In this work, we propose two approaches of hiding predictive association rules where the data sets are horizontally distributed and owned by collaborative but non-trusting parties. In particular, algorithms to hide the Collaborative Recommendation Association Rules (CRAR) and to merge the (sanitized) data sets are introduced. Performance and various side effects of the proposed approaches are analyzed numerically. Comparisons of non-trusting and trusting third-party approach are reported. Numerical results show that the non-trusting third-party approach has better processing time, with similar side effects to the trusting third-party approach.