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Privacy-Preserving Classification of Horizontally Partitioned Data via Random Kernels
We propose a novel privacy-preserving nonlinear support vector machine (SVM) classifier for a data matrix A whose columns represent input space features and whose individual rows are divided into groups of rows. Each ...
Privacy-Preserving Horizontally Partitioned Linear Programs
We propose a simple privacy-preserving reformulation of a linear program whose equality constraint matrix is partitioned into groups of rows. Each group of matrix rows and its corresponding right hand side vector are ...