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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∈R m×n whose rows are divided into privately owned blocks. Each block of rows belongs to a different entity that is unwilling to share its rows or make them public. We wish to obtain an accurate function …