Privacy-Preserving Classification of Horizontally Partitioned Data via Random Kernels
Abstract
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 group of rows belongs to an entity that is unwilling to share its rows or
make them public. Our classifier is based on the concept of a reduced kernel K(A,B?) where B? is
the transpose of a completely random matrix B. The proposed classifier, which is public but does
not reveal the privately-held data, has accuracy comparable to that of an ordinary SVM classifier
based on the entire data.
Subject
support vector machines
horizontally partitioned data
privacy preserving classification
Permanent Link
http://digital.library.wisc.edu/1793/64348Citation
07-03