Randomness Tests in Hostile Environments

Martin Goll; Shay Gueron

doi:10.1109/TDSC.2016.2537799

Randomness Tests in Hostile Environments

Martin Goll, Shay Gueron

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

Abstract

An acceptable way to assess the quality of an RNG (PRNG) is to apply a standard battery of statistical randomness tests to a sampled output. Such tests compare some observed properties of the sample to properties of a uniform distribution, with the hope to detect deviations from the expected behavior. Consider a (P)RNG that outputs M -bit values which, due to a failure or an attack, are coerced to a subset of {0, 1}^M of only 2ⁿ elements, for some n < M. Such outputs are predictable with a probability of at least 2^-n > 2^-M, but the standard randomness tests do not necessarily detect this behavior. We show here deterministic M-bit sequences (M = 128) that belong to a subset of size 2ⁿ, but pass the DIEHARD Battery of Tests of Randomness [1] and the NIST Statistical Test Suite [2], even with a relatively small value of n = 29. To address the difficulty, we propose a detection method that is feasible even for large values of n (e.g., n = 64). As a practical example, we apply our method to rule out the existence of the speculative stealthy hardware Trojan that is discussed in [3].

Original language	English
Pages (from-to)	289-294
Number of pages	6
Journal	IEEE Transactions on Dependable and Secure Computing
Volume	15
Issue number	2
DOIs	https://doi.org/10.1109/TDSC.2016.2537799
State	Published - 2016

Bibliographical note

Funding Information:
The authors thank Georg Becker and Christof Paar (authors of [3]) for sharing information on the parameters that were used with the NIST Statistical Test Suite, for confirming that our model matched the model that was used in [3], and for many other valuable discussions. They also thank two anonymous referess for their useful comments. This research was supported by the PQCRYPTO project, which was partially funded by the European Commission Horizon 2020 research Programme, grant #645622, and by the Blavatnik Interdisciplinary Cyber Research Center (ICRC) at the Tel Aviv University.

Publisher Copyright:
© 2004-2012 IEEE.

Keywords

Error-checking
random number generation
statistical computing
testing strategies

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/TDSC.2016.2537799

Cite this

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title = "Randomness Tests in Hostile Environments",

abstract = "An acceptable way to assess the quality of an RNG (PRNG) is to apply a standard battery of statistical randomness tests to a sampled output. Such tests compare some observed properties of the sample to properties of a uniform distribution, with the hope to detect deviations from the expected behavior. Consider a (P)RNG that outputs M -bit values which, due to a failure or an attack, are coerced to a subset of {0, 1}M of only 2n elements, for some n < M. Such outputs are predictable with a probability of at least 2-n > 2-M, but the standard randomness tests do not necessarily detect this behavior. We show here deterministic M-bit sequences (M = 128) that belong to a subset of size 2n, but pass the DIEHARD Battery of Tests of Randomness [1] and the NIST Statistical Test Suite [2], even with a relatively small value of n = 29. To address the difficulty, we propose a detection method that is feasible even for large values of n (e.g., n = 64). As a practical example, we apply our method to rule out the existence of the speculative stealthy hardware Trojan that is discussed in [3].",

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Randomness Tests in Hostile Environments

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Bibliographical note

Keywords

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