Differentially private learning of geometric concepts

Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 O~(1αϵklogd), where the domain is [d]×[d] and k is the number of edges in the union of polygons.
Original languageEnglish GB
Title of host publicationProceedings of the 36th International Conference on Machine Learning, PMLR
StatePublished - 2019


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