Privacy-Preserving Classification of Vertically Partitioned Data via Random Kernels
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We propose a novel privacy-preserving support vector machine (SVM) classifier for a data matrix A whose 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 kernel K(A,B?) where B? is the transpose of a random matrix B. The column blocks of B corresponding 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.
vertically partitioned data
support vector machines
privacy preserving classification