1 research outputs found
Privacy-Preserving Random Kernel Classification of Checkerboard Partitioned Data
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