Entropy Sources Based on Silicon Chips: True Random Number Generator and Physical Unclonable Function

Cao, Yuan; Liu, Wanyi; Lan, Qing; Liu, Bingqiang; Chen, Shuai; Ye, Jing; Xia, Xianzhao; Wang, Chao

doi:10.3390/e24111566

Cited by 12 publications

(5 citation statements)

References 133 publications

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“…It reflects the average amount of information per bit required to accurately predict the next bit in the sequence. The higher the entropy, the more unpredictable and information-rich the bitstream is, and vice versa [27,28]. One-bit probability numbers are calculated using the mathematical formula below:…”

Section: Entropy and Probability Calculatormentioning

confidence: 99%

“…Table 1 presents the probabilities of generating one, two, three, and four bits, as well as the entropy, for three different cases: three-, five-, and seven-stage ring oscillators, and the XOR of three bits using Equations ( 21)- (28). The entropy is a measure of the randomness of the system, and a higher entropy value indicates a more random system.…”

Section: Comparitive Study Of Entropy and Probability Numbersmentioning

confidence: 99%

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A High-Quality Random Number Generator Using Multistage Ring Oscillators and Fast Fourier Transform-Based Noise Extraction

Singh,

Hasan,

Azeemuddin

2024

Eng

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Random Numbers are widely employed in cryptography and security applications. This paper presents a novel approach to generate high-quality random bitstreams by harnessing the inherent noise properties of ring oscillators. We implemented ring oscillators with varying numbers of stages (3, 5, and 7), different geometries and different startup voltages in Cadence and recorded their total output power, which includes the cumulative noise effects. Subsequently, we exported these power measurements to MATLAB, where we applied a Fast Fourier Transform (FFT)-based technique to extract the total noise characteristics for each ring oscillator. Using the obtained noise data, we generated separate random bitstreams of 10 million bits for the 3-stage, 5-stage, and 7-stage ring oscillators. The final random bitstream, consisting of 10 million bits, was created by performing a bitwise XOR operation on the bitstreams generated by each ring oscillator. The degree of randomness of the generated bitstreams was assessed using the NIST 800-22 statistical test suite. Remarkably, the final random bitstream exhibited strong robustness and suitability for cryptographic applications. This innovative approach leverages the noise properties of ring oscillators to create reliable random bitstreams, offering potential applications in secure communications and cryptography. The results highlight the feasibility of using ring oscillators as noise sources for random bit generation and underscore their effectiveness in meeting stringent randomness criteria.

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Section: Entropy and Probability Calculatormentioning

confidence: 99%

Section: Comparitive Study Of Entropy and Probability Numbersmentioning

confidence: 99%

A High-Quality Random Number Generator Using Multistage Ring Oscillators and Fast Fourier Transform-Based Noise Extraction

Singh,

Hasan,

Azeemuddin

2024

Eng

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“…So, it makes sense to use the noise from a PUF as a source of entropy to extract a true random seed. In [16], a systematic review of various entropy sources that are based on silicon primitives, TRNGs and PUFs, is presented. Chaos-based, jitter-based, noise-based and metastability-based entropy sources are four types of sources that a silicon TRNG can use to harvest randomness.…”

Section: Sources Of Randomnessmentioning

confidence: 99%

Random Number Generators: Principles and Applications

Bikos,

Nastou,

Petroudis

et al. 2023

Cryptography

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In this paper, we present approaches to generating random numbers, along with potential applications. Rather than trying to provide extensive coverage of several techniques or algorithms that have appeared in the scientific literature, we focus on some representative approaches, presenting their workings and properties in detail. Our goal is to delineate their strengths and weaknesses, as well as their potential application domains, so that the reader can judge what would be the best approach for the application at hand, possibly a combination of the available approaches. For instance, a physical source of randomness can be used for the initial seed; then, suitable preprocessing can enhance its randomness; then, the output of preprocessing can feed different types of generators, e.g., a linear congruential generator, a cryptographically secure one and one based on the combination of one-way hash functions and shared key cryptoalgorithms in various modes of operation. Then, if desired, the outputs of the different generators can be combined, giving the final random sequence. Moreover, we present a set of practical randomness tests that can be applied to the outputs of random number generators in order to assess their randomness characteristics. In order to demonstrate the importance of unpredictable random sequences, we present an application of cryptographically secure generators in domains where unpredictability is one of the major requirements, i.e., eLotteries and cryptographic key generation.

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“…PUF serves as a mandatory component in the security protocol's authentication mechanism, generating challenge-response pairs (CRP) [6] [7]. The CRP is a critical element of device authentication, and the response is a non-linear function of the challenge and the specific features of the device or circuit [8] [9]. Since responses of the PUF are derived from hardware intrinsic attributes it is almost impossible to construct a model.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Architecture of DRAM PUF

Kumar,

Manoj Sindhwani,

Sachdeva

2023

JICS

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Cryptographic solutions based on traditional authentication methods are susceptible to several attacks on secret keys. Dynamic random access memory (DRAM)-based physical unclonable functions (PUF) are described as promising security building blocks to enable cryptography and authentication services. PUFs frequently suffer reliability concerns since they are sensitive to both internal and exterior noises. The need for enhanced resistance and dependability results in significant additional overheads. In this work, we proposed a DRAM-PUF based on the inclusion of selective hardware features in the computation. The proposed solution offers a higher number of challenge-response pairs (CRP) without additional circuitry. An innovative structure of PUF is presented in the paper which offers a reliable challenge-response pair module for authentication and authorization based on the ubiquitous nature of memory without the need for an additional circuit. DRAM PUF utilized the random startup value initialized by a capacitor followed by a random number generator. Our proposed PUF shows a reliability of 99.46% with temperature variation, a reliability of 99.5% with supply voltage variation, a uniqueness of 49.46%, a bit aliasing of 46.875%, and a uniformity of 47.65%.

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Entropy Sources Based on Silicon Chips: True Random Number Generator and Physical Unclonable Function

Cited by 12 publications

References 133 publications

A High-Quality Random Number Generator Using Multistage Ring Oscillators and Fast Fourier Transform-Based Noise Extraction

A High-Quality Random Number Generator Using Multistage Ring Oscillators and Fast Fourier Transform-Based Noise Extraction

Random Number Generators: Principles and Applications

An Innovative Architecture of DRAM PUF

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