On-the-fly tests for non-ideal true random number generators

Yang, Bohan; Rožić, Vladimir; Mentens, Nele; Verbauwhede, Ingrid

doi:10.1109/iscas.2015.7169072

Cited by 12 publications

(12 citation statements)

References 4 publications

(4 reference statements)

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“…Therefore, our implementation consists of three ensemble predictors -prediction-based tests for min-entropy estimation. In order to be consistent with previous work [3], the tests in this letter also report eight levels of min-entropy on a scale from 0 to 7. Assessment of min-entropy level in all tests is done by calculating the percentage of successful predictions.…”

Section: Set Of Implemented Predictorssupporting

confidence: 64%

“…Three types of non-ideal sources have been used: an unbiased generator with correlation between consecutive bits [3], a biased generator without correlation between consecutive bits [3] and a sampled ring oscillator. The theoretical value of the min-entropy of the entropy source is represented using continuous line in all figures of this section.…”

Section: Validation Of Implemented Testsmentioning

confidence: 99%

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Lightweight Prediction-Based Tests for On-Line Min-Entropy Estimation

Grujić

Rožić

Yang

et al. 2017

IEEE Embedded Syst. Lett.

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Abstract-Health tests (on-the-fly tests) play an important role in true random number generators because they are used to assess the quality of the bits produced by entropy source and to raise an alert when failures or attacks are detected. Most of classical tests are implemented as statistical tests. A set of new health tests based on predictors was presented by NIST (National Institute of Standards and Technology) in CHES 2015. These off-line tests attempt to predict the next output of the entropy source by trying to learn the patterns that the previously produced sequence of bits may possess. We provide the first integrated lightweight implementation of prediction-based tests for min-entropy estimation and verify their validity.Index Terms-Field-programmable gate arrays (FPGA), minentropy estimation, True random number generators (TRNG).

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Section: Set Of Implemented Predictorssupporting

confidence: 64%

Section: Validation Of Implemented Testsmentioning

confidence: 99%

Lightweight Prediction-Based Tests for On-Line Min-Entropy Estimation

Grujić

Rožić

Yang

et al. 2017

IEEE Embedded Syst. Lett.

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“…It is convenient to execute several tests in order to not overestimate the minimum entropy. As in [14], we have made some assumptions in order to simplify the implementation of these tests. The TRNG will generate one bit each clock cycle, which means that the size of the output space is two (0 or 1).…”

Section: On-line Testsmentioning

confidence: 99%

“…We have implemented the three aforementioned tests (Frequency Test, Collision Test and Partial Collection Test) following the scheme presented in [14]. The dataset length will be 2 15 .…”

Section: Tests Implementationmentioning

confidence: 99%

Dynamic control of entropy and power consumption in TRNGs for IoT applications

Martín

Heredia

Entrena

2018

IEICE Electron. Express

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In this article we present a study about how to obtain a trade-off between two important metrics for IoT systems-Security vs Power Consumption. More specifically, we have studied how the min-entropy of two True-Random number generators can be adjusted dynamically in order to reduce the power consumption while guaranteeing the integrity of the system. To that end, we make use of some statistical tests that are typically used to measure the quality of the RNGs. Clock-gating and enable-gating are the selected techniques to reduce the power consumption.

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“…Related work can be divided into two groups. The first group consists of implementations of standard statistical tests [2], [3], [4], [5], [6], [7], [8], [9]. These tests are originally designed for prototype evaluation, so some of them are too computationally intensive to be practical for on-the-fly testing.…”

Section: Introductionmentioning

confidence: 99%

TOTAL: TRNG On-the-fly Testing for Attack Detection using Lightweight Hardware

Yang

Rožić

Mentens

et al. 2016

Proceedings of the 2016 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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We present a design methodology for embedded tests of entropy sources. These tests are necessary to detect attacks and failures of true random number generators. The central idea of this work is to use an empirical design methodology consisting of two phases: collecting the data under attack and finding a useful statistical feature. In this work we focus on statistical features that are implementable in lightweight hardware. This is the first paper to address the design of on-the-fly tests based on the attack effects. The presented design methodology is illustrated with 2 examples: an elementary ring-oscillator based TRNG and a carrychain based TRNG. The effectiveness of the tests was confirmed on FPGA prototypes.

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On-the-fly tests for non-ideal true random number generators

Cited by 12 publications

References 4 publications

Lightweight Prediction-Based Tests for On-Line Min-Entropy Estimation

Lightweight Prediction-Based Tests for On-Line Min-Entropy Estimation

Dynamic control of entropy and power consumption in TRNGs for IoT applications

TOTAL: TRNG On-the-fly Testing for Attack Detection using Lightweight Hardware

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