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Title: Neural network method for base extension in residue number system
Authors: Babenko, M. G.
Бабенко, М. Г.
Shiriaev, E. M.
Ширяев, Е. М.
Golimblevskaia, E. I.
Голимблевская, Е. И.
Keywords: Computation theory;Control systems;Geometry;Numbering systems;Public key cryptography;Security of data;Neural networks
Issue Date: 2020
Publisher: CEUR-WS
Citation: Babenko, M., Shiriaev, E., Tchernykh, A., Golimblevskaia, E. Neural network method for base extension in residue number system // CEUR Workshop Proceedings. - 2020. - Volume 2638. - Pages 9-22
Series/Report no.: CEUR Workshop Proceedings
Abstract: Confidential data security is associated with the cryptographic primitives, asymmetric encryption, elliptic curve cryptography, homomorphic encryption, cryptographic pseudorandom sequence generators based on an elliptic curve, etc. For their efficient implementation is often used Residue Number System that allows executing additions and multiplications on parallel computing channels without bit carrying between channels. A critical operation in Residue Number System implementations of asymmetric cryptosystems is base extension. It refers to the computing a residue in the extended moduli without the application of the traditional Chinese Remainder Theorem algorithm. In this work, we propose a new way to perform base extensions using a Neural Network of a final ring. We show that it reduces 11.7% of the computational cost, compared with state-of-the-art approaches
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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