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Privacy-Preserving Linear and Nonlinear Approximation via Linear Programming
We propose a novel privacy-preserving random kernel approximation based on a data matrix A ? Rm�n whose rows are divided into privately owned blocks. Each block of rows belongs to a different entity that is unwilling to ...