文章基本信息

标题：Privacy-Preserving One-Class Support Vector Machine with Vertically Partitioned Data
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作者：Qiang Lin ; Huimin Pei ; Kuaini Wang 等
期刊名称：International Journal of Multimedia and Ubiquitous Engineering
印刷版ISSN：1975-0080
出版年度：2016
卷号：11
期号：5
页码：199-208
DOI：10.14257/ijmue.2016.11.5.18
出版社：SERSC
摘要：We establish a new model of privacy-preserving one-class support vector machine (SVM) based on vertically partitioned data. Every participant holds all the data with a part of attributes. They apply different random matrices to establish their own kernel matrix. By sharing these partial kernel matrices, we construct a global kernel matrix and establish linear and nonlinear privacy-preserving models. Experimental results on benchmark data sets verify the validity of the proposed models.
关键词：One-class SVM; random kernel; privacy-preserving; vertically partitioned ; data