Enhancing the secrecy of a cryptographic key generated using synchronized artificial neural networks1
| Autor: | V. F. Golikov, M. L. Radziukevich |
|---|---|
| Jazyk: | ruština |
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science Modulo cryptographic key Function (mathematics) QA75.5-76.95 Shared secret compression function Convolution cryptanalysis Electronic computers. Computer science Key (cryptography) shared secret Bitwise operation Algorithm Mixing (physics) Computer Science::Cryptography and Security synchronized artificial neural networks |
| Zdroj: | Informatika, Vol 17, Iss 1, Pp 102-108 (2020) |
| ISSN: | 1816-0301 |
| Popis: | The main options for the formation of a shared secret using synchronized artificial neural networks and possible patterns of behavior of a cryptanalyst are considered. To solve the problem of increasing the confidentiality of the generated shared secret, if it is used as a cryptographic key, it is proposed to use the mixing a certain number of results of individual synchronizations (convolution). As a mixing function, we consider the convolution of the vectors of network weights by bitwise addition modulo 2 of all the results of individual synchronizations. It is shown that the probability of success of a cryptanalyst is reduced exponentially with an increase of the number of terms in the convolution and can be chosen arbitrarily small. Moreover, the distribution law of the generated key after convolution is close to uniform and the uniformity increases with the number of terms in the convolution. |
| Databáze: | OpenAIRE |
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