@inproceedings{94c0e66e97bc4a9487d163555db0faec,
title = "Randomness for Randomness Testing",
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.",
keywords = "Property testing, Randomness testing, Truly random generator",
author = "Daniel Berend and Shlomi Dolev and Manish Kumar",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022 ; Conference date: 30-06-2022 Through 01-07-2022",
year = "2022",
month = jun,
day = "23",
doi = "10.1007/978-3-031-07689-3_11",
language = "English",
isbn = "9783031076886",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Cham",
pages = "153--161",
editor = "Shlomi Dolev and Amnon Meisels and Jonathan Katz",
booktitle = "Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings",
}