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arXiv · 2201.08522

Orthonormal Sketches for Secure Coded Regression

Neophytos Charalambides·Hessam Mahdavifar·Mert Pilanci·Alfred O. Hero III
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Abstract

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; in a centralized coded computing network. We focus on the special case of the \textit{Subsampled Randomized Hadamard Transform}, which we generalize to block sampling; and discuss how it can be used to secure the data. We illustrate the performance through numerical experiments.

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BibTeXRIS

Neophytos Charalambides, Hessam Mahdavifar, Mert Pilanci, Alfred O. Hero III. 2022-01-21. Orthonormal Sketches for Secure Coded Regression. https://arxiv.org/abs/2201.08522

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