  We present differentially private efficient algorithms for learning union of
polygons in the plane (which are not necessarily convex). Our algorithms
achieve $(\alpha,\beta)$-PAC learning and $(\epsilon,\delta)$-differential
privacy using a sample of size $\tilde{O}\left(\frac{1}{\alpha\epsilon}k\log
d\right)$, where the domain is $[d]\times[d]$ and $k$ is the number of edges in
the union of polygons.
