Differentially Private Learning of Geometric Concepts
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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 Õ(1/αϵk d), where the domain is [d]×[d] and k is the number of edges in the union of polygons.
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