Orthonormal Sketches for Secure Coded Regression
| Autor: | Neophytos Charalambides, Hessam Mahdavifar, Mert Pilanci, Alfred O. Hero |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Cryptography and Security E.3 E.4 G.1.2 G.1.3 Computer Science - Information Theory Information Theory (cs.IT) Numerical Analysis (math.NA) FOS: Mathematics FOS: Electrical engineering electronic engineering information engineering 65F10 65F45 68W15 68W20 68W25 68P27 68P30 Mathematics - Numerical Analysis Electrical Engineering and Systems Science - Signal Processing Cryptography and Security (cs.CR) |
| DOI: | 10.48550/arxiv.2201.08522 |
| Popis: | 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. Comment: 3 figures, 5 pages excluding appendices |
| Databáze: | OpenAIRE |
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