Entropy and Randomness: From Analogic to Quantum World

Simion, Emil

doi:10.1109/access.2020.2988658

Cited by 20 publications

(9 citation statements)

References 8 publications

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“…Several relevant contributions reviewed the state of the art, both from a mathematical [8] and an architectural [9] point of view; however, the available information addresses the problem at a high level, not focusing on particular technologies, such as Field Programmable Gate Arrays (FPGAs). On the other hand, programmable hardware and programmable System on Chip are gaining large adoption rate, also in a security-critical application, where high-quality random number generation is mandatory.…”

Section: Introduction: Random Number Generation For Securitymentioning

confidence: 99%

True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA

et al. 2021

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Random numbers are widely employed in cryptography and security applications. If the generation process is weak, the whole chain of security can be compromised: these weaknesses could be exploited by an attacker to retrieve the information, breaking even the most robust implementation of a cipher. Due to their intrinsic close relationship with analogue parameters of the circuit, True Random Number Generators are usually tailored on specific silicon technology and are not easily scalable on programmable hardware, without affecting their entropy. On the other hand, programmable hardware and programmable System on Chip are gaining large adoption rate, also in security critical application, where high quality random number generation is mandatory. The work presented herein describes the design and the validation of a digital True Random Number Generator for cryptographically secure applications on Field Programmable Gate Array. After a preliminary study of literature and standards specifying requirements for random number generation, the design flow is illustrated, from specifications definition to the synthesis phase. Several solutions have been studied to assess their performances on a Field Programmable Gate Array device, with the aim to select the highest performance architecture. The proposed designs have been tested and validated, employing official test suites released by NIST standardization body, assessing the independence from the place and route and the randomness degree of the generated output. An architecture derived from the Fibonacci-Galois Ring Oscillator has been selected and synthesized on Intel Stratix IV, supporting throughput up to 400 Mbps. The achieved entropy in the best configuration is greater than 0.995.

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Section: Introduction: Random Number Generation For Securitymentioning

confidence: 99%

True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA

et al. 2021

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“…In [8]- [10], key management in distributed systems is described. Simion [11] reviewed how mathematical entropy can be estimated and evaluated, of the construction mode (from technologies based on analogue procedures: thermal noise in a transistor to modern procedures: quantum devices), as well as to evaluate the security of binary sequence generators used for generating cryptographic keys or critical security parameters related to new technologies based on quantum principles. In [12], [13], a secret key exchange protocol that establishes a cryptographically secure connection is presented between two objects and presents a new scenario for symmetric key exchange for smart city applications, which protocol is based on the specific properties of Fuss-Catalan numbers and lattice path combinatorics.…”

Section: Methodsmentioning

confidence: 99%

Working with cryptographic key information

Mukhammadovich

Aloev²

2023

IJECE

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It is important to create a cryptographic system such that the encryption system does not depend on the secret storage of the algorithm that is part of it, but only on the private key that is kept secret. In practice, key management is a separate area of cryptography, which is considered a problematic area. This paper describes the main characteristics of working with cryptographic key information. In that, the formation of keys and working with cryptographic key information are stored on external media. The random-number generator for generating random numbers used for cryptographic key generation is elucidated. To initialize the sensor, a source of external entropy, mechanism “Electronic Roulette” (biological random number), is used. The generated random bits were checked on the basis of National Institute of Standards and Technology (NIST) statistical tests. As a result of the survey, the sequence of random bits was obtained from the tests at a value of P≥0.01. The value of P is between 0 and 1, and the closer the value of P is to 1, the more random the sequence of bits is generated. This means that random bits that are generated based on the proposed algorithm can be used in cryptography to generate crypto-resistant keys.

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“…In fact, there is currently a line of research that focuses on the development of new tests and batteries. Some of the most recent works in this direction are [34][35][36][37], among others. On the other hand, there are also various hardwarelevel tests that consist of physically examining the hardware used for generation to check if the generator is working properly and that there are no vulnerabilities or weak points that could compromise security [35].…”

Section: Key Aspects Of Pseudo-random Number Generatorsmentioning

confidence: 99%

Cryptographically Secured Pseudo-Random Number Generators: Analysis and Testing with NIST Statistical Test Suite

Almaraz Luengo,

Román Villaizán

2023

Mathematics

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There are several areas of knowledge in which (pseudo-)random numbers are necessary, for example, in statistical–mathematical simulation or in cryptography and system security, among others. Depending on the area of application, it will be necessary that the sequences used meet certain requirements. In general, randomness and uniformity conditions are required in the generated sequences, which are checked with statistical tests, and conditions on sequence unpredictability if the application is in security. In the present work, a literature review on cryptographically secure pseudo-random number generators (CSPRNGs) is carried out, they are implemented, and a critical analysis of their statistical quality and computational efficiency is performed. For this purpose, different programming languages will be used, and the sequences obtained will be checked by means of the NIST Statistical Test Suite (NIST STS). In addition, a user’s guide will be provided to allow the selection of one generator over another according to its statistical properties and computational implementation characteristics.

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Entropy and Randomness: From Analogic to Quantum World

Cited by 20 publications

References 8 publications

True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA

True Random Number Generator Based on Fibonacci-Galois Ring Oscillators for FPGA

Working with cryptographic key information

Cryptographically Secured Pseudo-Random Number Generators: Analysis and Testing with NIST Statistical Test Suite

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