An All-Digital and Jitter-Quantizing True Random Number Generator in SRAM-Based FPGAs

Xu, Xiaodong; Liang, Huaguo; Zhou, Kai; Ma, Gaoliang; Huang, Zhengfeng; Yi, Maoxiang; Ni, Tianming; Lu, Yingchun

doi:10.1109/ats.2018.00022

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…Unfortunately, the statistical dependency of the random variables η i depends on m, N and the probability mass function P. Accordingly, deriving a generic exact expression for F is not trivial. For increasing values of N and m, an heuristic approximated result can be achieved assuming the random variables η i statistically independent, obtaining a cumulative probability distribution (16) where the product is considering the cumulative probability distributions η i of the random variables η i , that are negative binomial with mean value and variance (9).…”

Section: B Low-complexity Maximum Generation Probability Estimationmentioning

confidence: 99%

A Stochastic Algorithm to Design Min-Entropy Tuning Controllers for True Random Number Generators

Addabbo

Fort

Moretti

et al. 2022

IEEE Trans. Circuits Syst. I

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We discuss a stochastic algorithm to design tuning controllers for cryptographic True Random Number Generators, compliant to NIST recommendations, as an effective low-complexity solution to counteract entropy variability in integrated architectures implementing tunable entropy sources. Taking as a reference the min-entropy concept, we discussed the proposal from both the theoretical and hardware design points of view, validating claims with proofs and experiments. Depending on the target accuracy, the proposed architecture is scalable, and its profitable use in TRNG design strongly depends on the kind of core entropy sources taken into account. Furthermore, we show that the low-complexity entropy measurement techniques exploited in this proposal can be used to design a legitimate alternative to the Adaptive Proportion Health Test recommended in the NIST 800.90B publication.

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Section: B Low-complexity Maximum Generation Probability Estimationmentioning

confidence: 99%

A Stochastic Algorithm to Design Min-Entropy Tuning Controllers for True Random Number Generators

Addabbo

Fort

Moretti

et al. 2022

IEEE Trans. Circuits Syst. I

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show abstract

“…In a typical TRNG structure, a random signal is first extracted from the target entropy source, then a digital sequence is generated by sampling the random signal, and finally the quality of the sequence is improved by a post-processing module. At present, entropy sources mainly include thermal noise, nuclear decay, cosmic radiation and other random physical phenomena [3], and thermal noise is the most widely used among many entropy sources [4][5][6][7]. Thermal noise is the voltage fluctuation generated by the random motion of electrons in the conductor.…”

Section: Introductionmentioning

confidence: 99%

A Low-overhead M_TRNG for Cryptographic Protection using Multiphase Sampling

Yao

Liang

2022

Preprint

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True Random Number Generator (TRNG) is built on hardware-based non-deterministic noise for generating keys, initialization vectors, and random numbers, and it plays an important role in various applications that require encryption protection.In this paper, a true random number generator based on MUX unit multi-phase sampling is proposed by studying the frequency jitter mechanism. The scheme is based on the "soft macro" design of the MUX unit, which replaces the traditional look-up table entropy scheme. It completes high-precision jitter sampling on the basis of ensuring the fairness of the TRNG entropy source, and can be well transplanted to a series of FPGAs. The proposed TRNG is verified on three FPGAs of Xilinx Virtex-6, Artix-7 and Virtex-7. The experimental results show that the generated random sequences are of good quality, passing the NIST SP800- 22 tests with higher p-value and NIST SP 800-90B tests with higher minimum entropy, while achieving 100Mbps throughput. It is worth mentioning that the resource overhead consumed by the proposed TRNG is small and unitary, only consuming 4 MUX units, 4 DFFs and 1 LUT unit, which has a good application prospect.

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Digital Low-Complexity Entropy Estimator Based on the Direct Assessment of Average Shannon Entropy

Addabbo,

Fort,

Spinelli

et al. 2025

Lecture Notes in Electrical Engineering

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An All-Digital and Jitter-Quantizing True Random Number Generator in SRAM-Based FPGAs

Cited by 7 publications

References 8 publications

A Stochastic Algorithm to Design Min-Entropy Tuning Controllers for True Random Number Generators

A Stochastic Algorithm to Design Min-Entropy Tuning Controllers for True Random Number Generators

A Low-overhead M_TRNG for Cryptographic Protection using Multiphase Sampling

Digital Low-Complexity Entropy Estimator Based on the Direct Assessment of Average Shannon Entropy

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