Privacy-preserving SVM classification on vertically partitioned data without secure multi-party computation

Authors:
Hu Yunhong;Fang Liang;He Guoping
Affiliations:
College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China and Department of Applied Mathematics, Yuncheng University, Yuncheng, Shanxi ...;College of Mathematics and Systems Science, Taishan University, Tai'an, P.R.China and Department of Mathematics, Shanghai Jiao Tong University, Shanghai, P.R.China;College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China
Venue:
ICNC'09 Proceedings of the 5th international conference on Natural computation
Year:
2009

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Abstract

With the development of information science and modern technology, it becomes more important about how to protect privacy information. In this paper, a novel privacy-preserving support vector machine (SVM) classifier is put forward for a vertically partitioned data. The proposed SVM classifier, which is public but does not reveal the privately-held data, has accuracy comparable to that of an ordinary SVM classifier based on the original data. We prove the feasibility of our algorithms by using matrix factorization theory and show the security without using the secure multi-party computation.