Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12258/18174
Title: Cryptographic primitives optimization based on the concepts of the residue number system and finite ring neural network
Authors: Babenko, M. G.
Бабенко, М. Г.
Shiriaev, E. M.
Ширяев, Е. М.
Golimblevskaia, E. I.
Голимблевская, Е. И.
Keywords: Cryptographic primitives;Residue number system (RNS);Encryption;Finite ring neural network;High-performance;Cryptography
Issue Date: 2021
Publisher: Springer Science and Business Media Deutschland GmbH
Citation: Tchernykh, A.; Babenko, M.; Pulido-Gaytan, B.; Shiriaev, E.; Golimblevskaia, E.; Avetisyan, A.; Hung, N. V.; Cortés-Mendoza, J. M. Cryptographic primitives optimization based on the concepts of the residue number system and finite ring neural network // Communications in Computer and Information Science. - 2021. - Том 1443. - Стр.: 241 - 253. - DOI 10.1007/978-3-030-85672-4_18
Series/Report no.: Communications in Computer and Information Science
Abstract: Data encryption has become a vital mechanism for data protection. One of the main challenges and an important target for optimization is the encryption/decryption speed. In this paper, we propose techniques for speeding up the software performance of several important cryptographic primitives based on the Residue Number System (RNS) and Finite Ring Neural Network (FRNN). RNS&FRNN reduces the computational complexity of operations with arbitrary-length integers such as addition, subtraction, multiplication, division by constant, Euclid division, and sign detection. To validate practical significance, we compare LLVM library implementations with state-of-the-art, high-performance, portable C++ NTL library implementations. The experimental analysis shows the superiority of the proposed optimization approach compared to the available approaches. For the NIST FIPS 186-5 digital signature algorithm, the proposed solution is 85% faster, even though the sign detection has low efficiency
URI: http://hdl.handle.net/20.500.12258/18174
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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