Differentially Private Learning of Geometric Concepts

We present differentially private efficient algorithms for learning union of polygons in the plane (which are not necessarily convex). Our algorithms achieve (α,β)-PAC learning and (ε,δ)-differential privacy using a sample of size tildeOleft(frac1αεklog dright), where the domain is [d]×[d] and k is the number of edges in the union of polygons.