On the jitter-to-fast-clock-period ratio in oscillator-based true random number generators

Bejar, Eduardo; Saldana, Julio; Raygada, Erick; Silva, Carlos

doi:10.1109/icecs.2017.8292100

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Therefore, even in the presence of parameter fluctuations, non-randomness in the random sequence can be eliminated through post-processing in the proposed scheme. Comparing physical RNGs based on thermal noise [13,14] and phase jitter [15][16][17], although they can generate high-quality random numbers, their outputs are unstable, and the generation rates are relatively low. The RNG proposed in this paper based on laser chaos has significantly improved generation rates and exhibits good stability.…”

Section: Internal Parameter Matchingmentioning

confidence: 99%

“…Physical random numbers [11,12] are generated by using physical phenomena in nature as entropy sources, exhibiting a high degree of unpredictability. The main types of physical entropy sources include thermal noise [13,14], phase jitter in oscillating signals [15][16][17], chaos [18][19][20][21], and others. Amplifying signals based on thermal noise, selecting appropriate detection thresholds, and subsequent signal processing can ultimately generate true random numbers.…”

Section: Introductionmentioning

confidence: 99%

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Random Numbers Generated Based on Dual-Channel Chaotic Light

Liu,

Mu,

Wang

et al. 2024

Electronics

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This paper presents a chaotic system based on novel semiconductor nanolasers (NLs), systematically analyzing its chaotic region and investigating the influence of key parameters on the unpredictability of chaotic output. This study found that under optical feedback conditions, NLs generate chaos across a wide range of feedback parameters, with the highly unpredictable region completely overlapping with the chaotic region. Further injection into the slave lasers enhances the chaotic output, expanding the range of unpredictability. Additionally, we analyzed the impact of internal parameter mismatch on the complexity of chaotic signals and found it to be similar to the scenario when parameters are matched. Using this chaotic system as an entropy source, we constructed a random number generator (RNG) and investigated the effects of internal parameters mismatch and differences in the injection parameters on the generator’s performance. The simulation results show that the RNG performs well under different parameter settings, and the generated random sequences pass all random number tests successfully. Therefore, this chaotic system can yield a high-complexity chaotic light source with appropriate parameter selection, and when combined with effective post-processing, it can generate high-quality random numbers. This is crucial for advancing the realization of small-sized, high-randomness RNGs.

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Section: Internal Parameter Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Random Numbers Generated Based on Dual-Channel Chaotic Light

Liu,

Mu,

Wang

et al. 2024

Electronics

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“…In true random number generators (TRNGs), there are three main types of entropy sources: thermal noise on resistors and capacitors [17,18], phase jitter of oscillating signals [19][20][21], chaos [22][23][24] and others, as shown in Figure 1. For the TRNGs based on thermal noise, the resistance noise is ampli ed to a suitable range by an ideal ampli er, and then processed by a comparator to compare the ampli ed noise voltage with the reference level to obtain a digital random signal [17].…”

Section: Introductionmentioning

confidence: 99%

A Survey on True Random Number Generators Based on Chaos

Tang

et al. 2019

Discrete Dynamics in Nature and Society

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With the rapid development of communication technology and the popularization of network, information security has been highly valued by all walks of life. Random numbers are used in many cryptographic protocols, key management, identity authentication, image encryption, and so on. True random numbers (TRNs) have better randomness and unpredictability in encryption and key than pseudorandom numbers (PRNs). Chaos has good features of sensitive dependence on initial conditions, randomness, periodicity, and reproduction. These demands coincide with the rise of TRNs generating approaches in chaos field. This survey paper intends to provide a systematic review of true random number generators (TRNGs) based on chaos. Firstly, the two kinds of popular chaotic systems for generating TRNs based on chaos, including continuous time chaotic system and discrete time chaotic system are introduced. The main approaches and challenges are exposed to help researchers decide which are the ones that best suit their needs and goals. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. We also devote a part of the paper to review TRNGs based on current-mode chaos for this problem. Finally, quantitative results are given for the described methods in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of TRNGs based on chaos.

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