| Title | Application of algorithmic information theory to calibrate tests of random number generators |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ryabko, Boris |
| Conference Name | 2021 XVII International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY) |
| Keywords | algorithmic information theory, Collaboration, composability, compositionality, Computational modeling, control systems, Generators, Human Behavior, human factors, Information security, information theoretic security, Information theory, Kolmogorov Complexity, physical random number generators, policy-based governance, pubcrawl, random number generator, randomness testing, Redundancy, resilience, Resiliency, Scalability, Standards, statistical test |
| Abstract | 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. |
| DOI | 10.1109/REDUNDANCY52534.2021.9606440 |
| Citation Key | ryabko_application_2021 |
Application of algorithmic information theory to calibrate tests of random number generators