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Single SMPC Invocation DPHelmet: Differentially Private Distributed Learning on a Large Scale. (arXiv:2211.02003v1 [cs.CR])
Nov. 4, 2022, 1:20 a.m. | Moritz Kirschte, Sebastian Meiser, Saman Ardalan, Esfandiar Mohammadi
cs.CR updates on arXiv.org arxiv.org
Distributing machine learning predictors enables the collection of
large-scale datasets while leaving sensitive raw data at trustworthy sites. We
show that locally training support vector machines (SVMs) and computing their
averages leads to a learning technique that is scalable to a large number of
users, satisfies differential privacy, and is applicable to non-trivial tasks,
such as CIFAR-10. For a large number of participants, communication cost is one
of the main challenges. We achieve a low communication cost by requiring only …
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