Kernel Affine Hull Machines for Differentially Private Learning. (arXiv:2304.01300v1 [cs.LG])

This paper explores the use of affine hulls of points as a means of
representing data via learning in Reproducing Kernel Hilbert Spaces (RKHS),
with the goal of partitioning the data space into geometric bodies that conceal
privacy-sensitive information about individual data points, while preserving
the structure of the original learning problem. To this end, we introduce the
Kernel Affine Hull Machine (KAHM), which provides an effective way of computing
a distance measure from the resulting bounded geometric body. KAHM …

autoencoders block body computing conceal critical data data points end information kernel machine machines measure privacy private problem sensitive information space