Privacy-Preserving Random Kernel Classification of Checkerboard Partitioned Data

Abstract

We propose a privacy-preserving support vector machine (SVM) classifier for a data matrix A whose input feature columns as well as individual data point rows are divided into groups belonging to different entities. Each entity is unwilling to make public its group of columns and rows. Our classifier utilizes the entire data matrix A while maintaining the privacy of each block. This classifier is based on the concept of a random kernel K(A,B?) where B? is the transpose of a random matrix B, as well as the reduction of a possibly complex pattern of data held by each entity into a checkerboard pattern. The proposed 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 based on the entire set of input features and data points all made public.