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dc.contributor.authorBabenko, M. G.-
dc.contributor.authorБабенко, М. Г.-
dc.contributor.authorShiriaev, E. M.-
dc.contributor.authorШиряев, Е. М.-
dc.contributor.authorGolimblevskaia, E. I.-
dc.contributor.authorГолимблевская, Е. И.-
dc.identifier.citationBabenko, M., Shiriaev, E., Tchernykh, A., Golimblevskaia, E. Neural network method for base extension in residue number system // CEUR Workshop Proceedings. - 2020. - Volume 2638. - Pages 9-22ru
dc.description.abstractConfidential 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 approachesru
dc.relation.ispartofseriesCEUR Workshop Proceedings-
dc.subjectComputation theoryru
dc.subjectControl systemsru
dc.subjectNumbering systemsru
dc.subjectPublic key cryptographyru
dc.subjectSecurity of dataru
dc.subjectNeural networksru
dc.titleNeural network method for base extension in residue number systemru
vkr.instИнститут математики и естественных наукru
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