Randomness for Randomness Testing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publicationCyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings
EditorsShlomi Dolev, Amnon Meisels, Jonathan Katz
PublisherSpringer Cham
Number of pages9
ISBN (Electronic)978-3-031-07689-3
ISBN (Print)9783031076886
StatePublished - 23 Jun 2022
Event6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022 - Beer Sheva, Israel
Duration: 30 Jun 20221 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13301 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
CityBeer Sheva


  • Property testing
  • Randomness testing
  • Truly random generator

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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