标题:Privacy-preserving SVM classification on arbitrarily partitioned data
作者:Hu, Yunhong ;He, Guoping ;Fang, Liang ;Tang, Jingyong
通讯作者:Hu, Y
作者机构:[Hu, Yunhong ] Department of Applied Mathematics, Yuncheng University, Yuncheng, Shanxi, 044000, China;[Fang, Liang ] College of Mathematics and Syste 更多
来源:Proceedings of the 2010 IEEE International Conference on Progress in Informatics and Computing, PIC 2010
出版年:2010
卷:1
页码:67-71
DOI:10.1109/PIC.2010.5687397
摘要: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 arbitrarily 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. ©2010 IEEE.
收录类别:EI
资源类型:会议论文
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