Privacy Preserving Learning Machine for Large Scale Datasets

Support vector machine (SVM) is widely used in pattern classification. In order to solve the privacy preserving problem in SVM, a privacy preserving learning machine for large scale datasets (PPLM) is proposed in this paper. First, core vector machine (CVM) is introduced for sampling the large scale datasets; then two points from different classes are chosen in the core set and the hyperplane orthogonal to the line connecting these two points is treated as the optimal separating hyperplane. Experimental results obtained from synthetic and standard datasets verify that the PPLM is effective and competitive.