Privacy Preserving Classification Algorithm BasedRandom Diffusion Map

摘要：

In this paper, a privacy preserving classification algorithm based random Diffusion Map is presented. We first alter the selection of the parameter dimension d and metaparameter fixed value ε for satisfying the security of privacy-preserving classification. Further the sensitive attributes are embedded into random(even higher) dimension feature space using random Diffusion Map, thus the sensitive attributes are transformed and protected. Because the transformed space dimension d and the ε are both stochastic, this algorithm is not easily be breached. In addition, diffusion Map can keep topology structure of dataset, so the classification precision after encryption are kept well. The experiment shows that the present method can provide sensitive information enough protect without much loss of the classification precision.

作者: Wei Lu

作者单位: School of Information Technology, Beijing Normal University Zhuhai Campus School of Information Technology of Beijing Normal University Zhuhai Campus,Zhuhai,China,519085

会议类型: 国际会议

会议名称: Fifth International Conference on Semantics,Knowledge and Grid(第五届语义、知识与网格国际会议 SKG 2009)

会议地点: 珠海

会议语种:英文

页码: 318-321

在线出版日期: 2009-10-12（万方平台首次上网日期，不代表论文的发表时间）