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Montreal, Quebec, Canada
July 25, 2005 to July 27, 2005
ISBN: 0-7695-2404-4
pp: 339-343
Yiqun Huang , Huazhong University of Science and Technology
Zhengding Lu , Huazhong University of Science and Technology
Heping Hu , Huazhong University of Science and Technology
ABSTRACT
There have been growing interests in privacy preserving data mining. Secure multiparty computation (SMC) is often used to give a solution. When data is vertically partitioned scalar product is a feasible tool to securely discover frequent itemsets of association rule mining. However, there may be disparity among the securities of different parties. To obtain equal privacy, the security of some parties may be lowered. This paper discusses the disharmony between the securities of two parties. The scalar product of two parties from the point of view of matrix computation is described. We present one algorithm for completely two-party computation of scalar product. Then we give a method of security improvement for both parties.
INDEX TERMS
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CITATION
Yiqun Huang, Zhengding Lu, Heping Hu, "A Method of Security Improvement for Privacy Preserving Association Rule Mining over Vertically Partitioned Data", IDEAS, 2005, Proceedings. 9th International Database Engineering and Applications Symposium (IDEAS 2005), Proceedings. 9th International Database Engineering and Applications Symposium (IDEAS 2005) 2005, pp. 339-343, doi:10.1109/IDEAS.2005.6
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