Privacy-Preserving S VM of Horizontally Partitioned Data for Linear Classification

摘要：

When we use support vector machine (SVM) to solve the classical classification problem, we should know all data. However, the data sometimes can reveal private information which is protected by laws. So recently, there has been growing focus on finding solutions to get a SVM classifier without revealing any information of the privately-held data. In this paper, we propose a new method which is ameliorated from the usual SVM to solve this problem over horizontally partitioned data which can protect the private information of the data completely. And under some special conditions, the model provided in this paper can achieve same accuracy with the usual SVM constituted by the original data. The experiments on real datasets show that the classification accuracy of our proposed method on the protected data is approximate to the SVM classifier on the original data.

关键词： Privacy-Preserving classification support vector machine (SVM) horizontally partitioned data

作者: Jingjing Qiang Bing Yang Qian Li Ling Jing

作者单位: Department of Applied Mathematics College of Science, China Agricultural University 100083, Beijing, P.R. China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 2802-2806

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）