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Privacy-preserving classification of vertically partitioned data via random kernels
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 …