Privacy-Preserving Classification of Vertically Partitioned Data via Random Kernels
Abstract
We propose a novel privacy-preserving support vector machine (SVM) classifier for a data matrix A whose
input feature columns are divided into groups belonging to different entities. Each entity is unwilling to share
its group of columns or make it public. Our classifier is based on the concept of a reduced kernel K(A,B?)
where B? is the transpose of a random matrix B. The column blocks of B corresponding to the different
entities are privately generated by each entity and never made public. The proposed linear or nonlinear SVM
classifier, which is public but does not reveal any of the privately-held data, has accuracy comparable to that
of an ordinary SVM classifier that uses the entire set of input features directly.
Subject
vertically partitioned data
support vector machines
privacy preserving classification
Permanent Link
http://digital.library.wisc.edu/1793/64346Citation
07-02