A comparative study and analysis of some pseudorandom number generator algorithms

Sinha, Shobhit; Islam, SK Hafizul; Obaidat, Mohammad S.

doi:10.1002/spy2.46

Cited by 7 publications

(4 citation statements)

References 11 publications

(21 reference statements)

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“…In such use cases, the stream ciphers [16] will also not prove efficient due to the length of the payload. Moreover, a pseudorandom number generator (PRNG) [17] will be required to ensure their correct operation. The PRNG provides necessary random values on an ongoing basis.…”

Section: Related Workmentioning

confidence: 99%

Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding

Krokosz,

Rykowski,

Zajęcka

et al. 2023

Sensors

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Modern, commonly used cryptosystems based on encryption keys require that the length of the stream of encrypted data is approximately the length of the key or longer. In practice, this approach unnecessarily complicates strong encryption of very short messages commonly used for example in ultra-low-power and resource-constrained wireless network sensor nodes based on microcontrollers (MCUs). In such cases, the data payload can be as short as a few bits of data while the typical length of the key is several hundred bits or more. The article proposes an idea of employing a complex of two algorithms, initially applied for data compression, acting as a standard-length encryption key algorithm to increase the transmission security of very short data sequences, even as short as one or a few bytes. In this article, we present and evaluate an approach that uses LZW and Huffman coding to achieve data transmission obfuscation and a basic level of security.

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Section: Related Workmentioning

confidence: 99%

Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding

Krokosz,

Rykowski,

Zajęcka

et al. 2023

Sensors

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“…It is impossible to consider true randomness in PRNG-generated [5] sequences as they are implemented by software, based on mathematical algorithms and determined by an initial (seed) value. In contrast, the TRNG and QRNG, based on unpredictable physical means, generate the true randomness in the sequence of random numbers.…”

Section: Category Iii: Research Objectives Of Quantum Rngs For Crypto...mentioning

confidence: 99%

“…Pseudorandom number generators (PRNGs) [3,4] are based on algorithms for generating seemingly random numbers, which are determined by their seed, or initial value to enhance security. Shobhit Sinha et al [5] compared various PRNGs based on their statistical randomness and cryptographic security, concluding that some PRNGs performed RNGs' randomness critically depends on the type of RNG.…”

Section: Introductionmentioning

confidence: 99%

Quantum Randomness in Cryptography—A Survey of Cryptosystems, RNG-Based Ciphers, and QRNGs

Saini¹,

Tsokanos²,

Kirner³

2022

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Cryptography is the study and practice of secure communication with digital data and focuses on confidentiality, integrity, and authentication. Random number generators (RNGs) generate random numbers to enhance security. Even though the cryptographic algorithms are public and their strength depends on the keys, cryptoanalysis of encrypted ciphers can significantly contribute to the unveiling of the cipher’s key. Therefore, to ensure high data security over a network, researchers need to improve the randomness of keys as they develop cryptosystems. Quantum particles have a leading edge in advancing RNG technology as they can provide true randomness, unlike pseudo-random numbers generators (PRNGs). In order to increase the level of the security of cryptographic systems based on random numbers, this survey focuses on three objectives: Cryptosystems with related cryptographic attacks, RNG-based cryptosystems, and the design of quantum random number generators (QRNGs). This survey aims to provide researchers with information about the importance of RNG-based ciphers and various research techniques for QRNGs that can incorporate quantum-based true randomness in cryptosystems.

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“…In [27], different LCG PRNGS algorithms presented different results in the NIST test. Results showed that compared with PRNGs, all LCGs exhibited a poor performance.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Cryptographic Pseudorandom Number Generators

et al. 2019

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A comparative study and analysis of some pseudorandom number generator algorithms

Cited by 7 publications

References 11 publications

Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding

Cryptographic Algorithms with Data Shorter than the Encryption Key, Based on LZW and Huffman Coding

Quantum Randomness in Cryptography—A Survey of Cryptosystems, RNG-Based Ciphers, and QRNGs

Performance Analysis of Cryptographic Pseudorandom Number Generators

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