Differentially Private Learning of Geometric Concepts

Abstract

We present differentially private efficient algorithms for learning union of polygons in the plane (which are not necessarily convex). Our algorithms achieve (,)-PAC learning and (,)-differential privacy using a sample of size O(1k d), where the domain is [d][d] and k is the number of edges in the union of polygons.

Tasks

Reproductions