Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
The time-adaptive statistical testing for random number generators. / Ryabko, Boris; Zhuravlev, Viacheslav.
Proceedings of 2020 International Symposium on Information Theory and its Applications, ISITA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 344-347 9366166 (Proceedings of 2020 International Symposium on Information Theory and its Applications, ISITA 2020).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - The time-adaptive statistical testing for random number generators
AU - Ryabko, Boris
AU - Zhuravlev, Viacheslav
N1 - Funding Information: ACKNOWLEDGEMENT The research was supported by the Russian Foundation for Basic Research (grant no. 18-29-03005). Publisher Copyright: © 2020 IEICE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/10/24
Y1 - 2020/10/24
N2 - Currently, there are dozens of random number generators (RNGs) and hundreds of statistical tests designed to test the generators. These tests are often combined into so-called batteries, each of which contains from a dozen to more than a hundred tests. When a battery test is used, it is applied to a sequence generated by the RNG, and the calculation time is determined by the length of thesequence and the number of tests. Generally speaking, the longer the sequence, the smaller deviations from randomness can be found by a specific test. So, when a battery is applied, on the one hand,the "better"tests are in the battery, the more chances to reject a "bad"RNG. On the other hand, the larger the battery, the less time can be spent on each test and, therefore, the shorter the testsequence. In turn, this reduces the ability to find small deviations from randomness. To reduce this trade-off, we propose an adaptive way to use batteries (and other sets) of tests that can be usedin such a way as to increase the testing power.
AB - Currently, there are dozens of random number generators (RNGs) and hundreds of statistical tests designed to test the generators. These tests are often combined into so-called batteries, each of which contains from a dozen to more than a hundred tests. When a battery test is used, it is applied to a sequence generated by the RNG, and the calculation time is determined by the length of thesequence and the number of tests. Generally speaking, the longer the sequence, the smaller deviations from randomness can be found by a specific test. So, when a battery is applied, on the one hand,the "better"tests are in the battery, the more chances to reject a "bad"RNG. On the other hand, the larger the battery, the less time can be spent on each test and, therefore, the shorter the testsequence. In turn, this reduces the ability to find small deviations from randomness. To reduce this trade-off, we propose an adaptive way to use batteries (and other sets) of tests that can be usedin such a way as to increase the testing power.
UR - http://www.scopus.com/inward/record.url?scp=85102630268&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85102630268
T3 - Proceedings of 2020 International Symposium on Information Theory and its Applications, ISITA 2020
SP - 344
EP - 347
BT - Proceedings of 2020 International Symposium on Information Theory and its Applications, ISITA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Symposium on Information Theory and its Applications, ISITA 2020
Y2 - 24 October 2020 through 27 October 2020
ER -
ID: 28753466