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Orthonormal Sketches for Secure Coded Regression}. (arXiv:2201.08522v1 [cs.IT])
Jan. 24, 2022, 2:20 a.m. | Neophytos Charalambides, Hessam Mahdavifar, Mert Pilanci, Alfred O. Hero III
cs.CR updates on arXiv.org arxiv.org
In this work, we propose a method for speeding up linear regression
distributively, while ensuring security. We leverage randomized sketching
techniques, and improve straggler resilience in asynchronous systems.
Specifically, we apply a random orthonormal matrix and then subsample in
\textit{blocks}, to simultaneously secure the information and reduce the
dimension of the regression problem. In our setup, the transformation
corresponds to an encoded encryption in an \textit{approximate} gradient coding
scheme, and the subsampling corresponds to the responses of the non-straggling
workers; …
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