Private center points and learning of halfspaces

Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer

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


We present a private agnostic learner for halfspaces over an arbitrary finite domain X⊂\Rd with sample complexity poly(d,2log∗|X|) The building block for this learner is a differentially private algorithm for locating an approximate center point of m>poly(d,2log∗|X|) points – a high dimensional generalization of the median function. Our construction establishes a relationship between these two problems that is reminiscent of the relation between the median and learning one-dimensional thresholds [Bun et al. FOCS ’15]. This relationship suggests that the problem of privately locating a center point may have further applications in the design of differentially private algorithms. We also provide a lower bound on the sample complexity for privately finding a point in the convex hull. For approximate differential privacy, we show a lower bound of m=Ω(d+log∗|X|) whereas for pure differential privacy m=Ω(dlog|X|)
Original languageEnglish GB
Title of host publicationProceedings of Machine Learning Research
Subtitle of host publication32nd Annual Conference on Learning Theory
EditorsAlina Beygelzimer, Daniel Hsu
StatePublished - 2019

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