SÝS, Marek, Zdeněk ŘÍHA, Václav MATYÁŠ, Kinga MÁRTON and Alin SUCIU. On the interpretation of results from the NIST statistical test suite. Romanian Journal of Information Science and Technology. Publishing House of the Romanian Academy, 2015, vol. 18, No 1, p. 18-32. ISSN 1453-8245.
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Basic information
Original name On the interpretation of results from the NIST statistical test suite
Authors SÝS, Marek (703 Slovakia, guarantor, belonging to the institution), Zdeněk ŘÍHA (203 Czech Republic, belonging to the institution), Václav MATYÁŠ (203 Czech Republic, belonging to the institution), Kinga MÁRTON (642 Romania) and Alin SUCIU (642 Romania).
Edition Romanian Journal of Information Science and Technology, Publishing House of the Romanian Academy, 2015, 1453-8245.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Romania
Confidentiality degree is not subject to a state or trade secret
Impact factor Impact factor: 0.269
RIV identification code RIV/00216224:14330/15:00081363
Organization unit Faculty of Informatics
UT WoS 000369852700002
Keywords in English Hypothesis testing; NIST STS; Statistical randomness testing
Changed by Changed by: prof. RNDr. Václav Matyáš, M.Sc., Ph.D., učo 344. Changed: 15/8/2019 13:04.
Abstract
NIST Statistical Test Suite is an important testing suite for randomness analysis often used for formal certifications or approvals. Documentation of the NIST STS gives some guidance on how to interpret results of the NIST STS but interpretation is not clear enough or it uses just approximated values. Moreover NIST considers data to be random if all tests are passed yet even truly random data shows a high probability (80%) of failing at least one NIST STS test. If data fail some tests the NIST STS recommends the analysis of different samples. We analysed 819200 sequences (100 GB of data) produced by a physical source of randomness (quantum random number generator) in order to interpret results computed without analysing any additional samples. The results indicate that data can be still considered random for the significance level a = 0.01 if they fail less than 7 NIST STS tests, 7 tests of uniformity of p-values (100 sequences) or 10 tests of proportion of passing sequences. We have also defined a more accurate interval of acceptable proportions computed with a new constant (2.6 instead of 3) for which 1000 sequences can be considered random if they fail less than 7 tests of proportion.
Links
EE2.3.30.0037, research and development projectName: Zaměstnáním nejlepších mladých vědců k rozvoji mezinárodní spolupráce
GBP202/12/G061, research and development projectName: Centrum excelence - Institut teoretické informatiky (CE-ITI) (Acronym: CE-ITI)
Investor: Czech Science Foundation
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