aiqbench analysis
This paper gives the first computationally efficient differentially private algorithms for agnostically learning halfspaces, a basic class of linear classifiers, under Gaussian and log-concave data distributions. Its main claim is that privacy adds no extra sample complexity in the studied settings, which is a stronger result than the usual privacy-utility tradeoff. Readers should note that these are theoretical guarantees under distributional assumptions, so they do not yet say how the methods perform on real-world data.
Privacy for Free in Efficient Agnostic Halfspace Learning
arXiv:2611.05208v1 Announce Type: new Abstract: We provide the first computationally efficient, differentially private algorithms for agnostically learning halfspaces. In particular, we study three different settings of agnostic learning: improper learning under Gaussian and log-concave marginals; proper learning under Gaussian marginals; and semi-agnostic learning under Gaussian marginals. In all three aforementioned settings, the added cost in the sample complexity due to privacy matches the respective non-private sample complexity, implying that we get privacy for free.
Abstract:We provide the first computationally efficient, differentially private algorithms for agnostically learning halfspaces. In particular, we study three different settings of agnostic learning: improper learning under Gaussian and log-concave marginals; proper learning under Gaussian marginals; and semi-agnostic learning under Gaussian marginals. In all three aforementioned settings, the added cost in the sample complexity due to privacy matches the respective non-private sample complexity, implying that we get privacy for free.
Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2611.05208 [cs.LG]
(or arXiv:2611.05208v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2611.05208 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 166 words
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