Randomness for Randomness Testing

Daniel Berend; Shlomi Dolev; Manish Kumar

doi:10.1007/978-3-031-07689-3_11

Randomness for Randomness Testing

Daniel Berend
, Shlomi Dolev
, Manish Kumar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Given a binary sequence, one may inquire whether it is produced by a true random source. There are several tests designed to answer this question, such as the statistical test suite of the National Institute of Standard and Technology (NIST) and the Diehard tests. The problem is that, given deterministic tests of randomization, an adversary may know/learn, the adversary may tailor a non-random (deterministic) sequence, guided by the deterministic tests, that passes the tests. We suggest to use a true random source for randomness tests and thus make the tests significantly harder to being misled. We design tests that use true random sources and demonstrate their ability to detect non-random sequences that NIST classifies as random.

Original language	English
Title of host publication	Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings
Editors	Shlomi Dolev, Amnon Meisels, Jonathan Katz
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	153-161
Number of pages	9
ISBN (Print)	9783031076886
DOIs	https://doi.org/10.1007/978-3-031-07689-3_11
State	Published - 1 Jan 2022
Event	6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022 - Beer Sheva, Israel Duration: 30 Jun 2022 → 1 Jul 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13301 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
Country/Territory	Israel
City	Beer Sheva
Period	30/06/22 → 1/07/22

Keywords

Property testing
Randomness testing
Truly random generator

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-07689-3_11

Cite this

Berend, D., Dolev, S., & Kumar, M. (2022). Randomness for Randomness Testing. In S. Dolev, A. Meisels, & J. Katz (Eds.), Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings (pp. 153-161). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13301 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-07689-3_11

Berend, Daniel ; Dolev, Shlomi ; Kumar, Manish. / Randomness for Randomness Testing. Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings. editor / Shlomi Dolev ; Amnon Meisels ; Jonathan Katz. Springer Science and Business Media Deutschland GmbH, 2022. pp. 153-161 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Given a binary sequence, one may inquire whether it is produced by a true random source. There are several tests designed to answer this question, such as the statistical test suite of the National Institute of Standard and Technology (NIST) and the Diehard tests. The problem is that, given deterministic tests of randomization, an adversary may know/learn, the adversary may tailor a non-random (deterministic) sequence, guided by the deterministic tests, that passes the tests. We suggest to use a true random source for randomness tests and thus make the tests significantly harder to being misled. We design tests that use true random sources and demonstrate their ability to detect non-random sequences that NIST classifies as random.",

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Berend, D , Dolev, S & Kumar, M 2022, Randomness for Randomness Testing. in S Dolev, A Meisels & J Katz (eds), Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13301 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 153-161, 6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022, Beer Sheva, Israel, 30/06/22. https://doi.org/10.1007/978-3-031-07689-3_11

Randomness for Randomness Testing. / Berend, Daniel ; Dolev, Shlomi; Kumar, Manish.
Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings. ed. / Shlomi Dolev; Amnon Meisels; Jonathan Katz. Springer Science and Business Media Deutschland GmbH, 2022. p. 153-161 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13301 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Given a binary sequence, one may inquire whether it is produced by a true random source. There are several tests designed to answer this question, such as the statistical test suite of the National Institute of Standard and Technology (NIST) and the Diehard tests. The problem is that, given deterministic tests of randomization, an adversary may know/learn, the adversary may tailor a non-random (deterministic) sequence, guided by the deterministic tests, that passes the tests. We suggest to use a true random source for randomness tests and thus make the tests significantly harder to being misled. We design tests that use true random sources and demonstrate their ability to detect non-random sequences that NIST classifies as random.

AB - Given a binary sequence, one may inquire whether it is produced by a true random source. There are several tests designed to answer this question, such as the statistical test suite of the National Institute of Standard and Technology (NIST) and the Diehard tests. The problem is that, given deterministic tests of randomization, an adversary may know/learn, the adversary may tailor a non-random (deterministic) sequence, guided by the deterministic tests, that passes the tests. We suggest to use a true random source for randomness tests and thus make the tests significantly harder to being misled. We design tests that use true random sources and demonstrate their ability to detect non-random sequences that NIST classifies as random.

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Berend D , Dolev S, Kumar M. Randomness for Randomness Testing. In Dolev S, Meisels A, Katz J, editors, Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 153-161. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-07689-3_11

Randomness for Randomness Testing

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Keywords

ASJC Scopus subject areas

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