A new privacy-preserving proximal support vector machine for classification of vertically partitioned data

作者：Sun Li; Mu Wei Song; Qi Biao; Zhou Zhi Jian^*

来源：International Journal of Machine Learning and Cybernetics, 2015, 6(1): 109-118.

DOI：10.1007/s13042-014-0245-1

• Privacy preserving classification
• Global random reduced kernel
• Proximal support vector machine
• Vertically partitioned data

A new privacy-preserving proximal support vector machine (P-3 SVM) is formulated for classification of vertically partitioned data. Our classifier is based on the concept of global random reduced kernel which is composed of local reduced kernels. Each of them is computed using local reduced matrix with Gaussian perturbation, which is privately generated by only one of the parties, and never made public. This formulation leads to an extremely simple and fast privacy-preserving algorithm, for generating a linear or nonlinear classifier that merely requires the solution of a single system of linear equations. Comprehensive experiments are conducted on multiple publicly available benchmark datasets to evaluate the performance of the proposed algorithms and the results indicate that: (a) Our P-3 SVM achieves better performance than the recently proposed privacy-preserving SVM via random kernels in terms of both classification accuracy and computational time. (b) A significant improvement of accuracy is attained by our P-3 SVM when compared to classifiers generated only using each party's own data.

• 出版日期2015-2
• 单位中国农业大学