Privacy-preserving linear and nonlinear approximation via linear programming
نویسندگان
چکیده
منابع مشابه
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 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 approximation for a given y ∈ Rm corresponding to each of the m rows of A. Our approximation of y is a re...
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ژورنال
عنوان ژورنال: Optimization Methods and Software
سال: 2013
ISSN: 1055-6788,1029-4937
DOI: 10.1080/10556788.2012.710615