Privacy-preserving classification of vertically partitioned data via random kernels
Olvi L. Mangasarian, Edward W. Wild, Glenn Fung · ACM Transactions on Knowledge Discovery from Data · 2008
We propose a novel privacy-preserving support vector machine (SVM) classifier for a data matrixAwhose input feature columns are divided into groups belonging to different entities. Each entity is unwilling to share its group of columns or make it public. Our classifier is based on the concept of a reduced kernelK(A,B′), whereB′ is the transpose of a random matrixB. The column blocks ofBcorresponding to the different entities are privately generated by each entity and never made public. The proposed linear or nonlinear SVM classifier, which is public but does not reveal any of the privately held data, has accuracy comparable to that of an ordinary SVM classifier that uses the entire set of input features directly.