Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Application of algorithmic information theory to calibrate tests of random number generators. / Ryabko, Boris.
2021 17th International Symposium Problems of Redundancy in Information and Control Systems, REDUNDANCY 2021. Institute of Electrical and Electronics Engineers Inc., 2021. стр. 61-65 (2021 17th International Symposium Problems of Redundancy in Information and Control Systems, REDUNDANCY 2021).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
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TY - GEN
T1 - Application of algorithmic information theory to calibrate tests of random number generators
AU - Ryabko, Boris
N1 - Funding Information: Research was supported by Russian Foundation for Basic Research (grant no. 18-29-03005). Publisher Copyright: © 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Currently, statistical tests for random number generators (RNGs) are widely used in practice, and some of them are even included in information security standards. But despite the popularity of RNGs, consistent tests are known only for stationary ergodic deviations of randomness (a test is consistent if it detects any deviations from a given class when the sample size goes to infinity). However, the model of a stationary ergodic source is too narrow for some RNGs, in particular, for generators based on physical effects. In this article, we propose computable consistent tests for some classes of deviations more general than stationary ergodic and describe some general properties of statistical tests. The proposed approach and the resulting test are based on the ideas and methods of information theory.
AB - Currently, statistical tests for random number generators (RNGs) are widely used in practice, and some of them are even included in information security standards. But despite the popularity of RNGs, consistent tests are known only for stationary ergodic deviations of randomness (a test is consistent if it detects any deviations from a given class when the sample size goes to infinity). However, the model of a stationary ergodic source is too narrow for some RNGs, in particular, for generators based on physical effects. In this article, we propose computable consistent tests for some classes of deviations more general than stationary ergodic and describe some general properties of statistical tests. The proposed approach and the resulting test are based on the ideas and methods of information theory.
KW - algorithmic information theory
KW - information theory
KW - Kolmogorov complexity
KW - physical random number generators
KW - random number generator
KW - randomness testing
KW - statistical test
UR - http://www.scopus.com/inward/record.url?scp=85123292341&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/1d1fb93a-7259-3a55-872d-97bb6c91e6fc/
U2 - 10.1109/REDUNDANCY52534.2021.9606440
DO - 10.1109/REDUNDANCY52534.2021.9606440
M3 - Conference contribution
AN - SCOPUS:85123292341
SN - 9781665433082
T3 - 2021 17th International Symposium Problems of Redundancy in Information and Control Systems, REDUNDANCY 2021
SP - 61
EP - 65
BT - 2021 17th International Symposium Problems of Redundancy in Information and Control Systems, REDUNDANCY 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Symposium Problems of Redundancy in Information and Control Systems, REDUNDANCY 2021
Y2 - 25 October 2021 through 29 October 2021
ER -
ID: 35304384