Parallel chaotic hash function based on the shuffle-exchange network

Teh, Je Sen; Samsudin, Azman; Akhavan, Amir

doi:10.1007/s11071-015-2049-6

Cited by 62 publications

(26 citation statements)

References 25 publications

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“…It also has random behavior, ergodicity, diffusion and confusion characteristics that are analogous to the requirements of cryptographic algorithms. They have been utilized in various areas of cryptography such as encryption algorithms [29][30][31], hash functions [32][33][34][35], key exchange protocols [36][37][38] and many more. These qualities are suitable for the design of a TRNG as any unpredictable behavior will be further amplified, contributing to higher entropy.…”

Section: Chaos Theorymentioning

confidence: 99%

GPUs and chaos: a new true random number generator

et al. 2015

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For applications where security and unpredictability is of utmost importance, true random number generators (TRNGs) play a heavy role compared to its pseudo-random counterparts. Most TRNGs obtain randomness from physical phenomena such as radio noise, radioactive decay or thermal noise that are unpredictable. These applications usually require external hardware to extract entropy and convert them into digital signals. This paper introduces a TRNGs that utilizes graphics processing units as the source of entropy. Its unpredictable behavior is harnessed by computing chaotic maps that are highly sensitive to slight changes to their control parameters and have pseudo-random behavior. A simple post-processing function based on modular addition and XOR is then used to achieve an unbiased output. The security of the proposed TRNG is evaluated using statistical test suites such as the NIST SP 800-22, DIEHARD and ENT, as well as entropy analysis to determine unpredictability. Results indicate that the proposed TRNG has strong statistical quality

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Section: Chaos Theorymentioning

confidence: 99%

GPUs and chaos: a new true random number generator

et al. 2015

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“…Jiteurtragool et al [34], proposed a topologically simple keyed hash function based on circular chaotic sinusoidal map network that uses more complex map, i.e., the Sine map. In 2014, Teh et al, [35] introduced a parallel chaotic hash function based on the shuffle-exchange network that runs in parallel to improve hashing speed. In 2015, Abdoun et al, [36,37] proposed a new efficient structure that consists of two parts: an efficient chaotic generator and a three or two-layer neural network.…”

Section: Introductionmentioning

confidence: 99%

Design and security analysis of two robust keyed hash functions based on chaotic neural networks

Abdoun

Assad

Déforges

et al. 2019

J Ambient Intell Human Comput

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In this paper, we designed, implemented, and analyzed the performance, in terms of security and speed, of two proposed keyed Chaotic Neural Network (CNN) hash functions based on Merkle-Dåmgard (MD) construction with three output schemes: CNN-Matyas-Meyer-Oseas, Modified CNN-Matyas-Meyer-Oseas, and CNN-Miyaguchi-Preneel. The first hash function's structure is composed of two-layer chaotic neural network while the structure of the second hash function is formed of one-layer chaotic neural network followed by non-linear layer functions. The obtained results of several statistical tests and cryptanalytic analysis highlight the robustness of the proposed keyed CNN hash functions, which is fundamentally due to the strong non-linearity of both the chaotic systems and the neural networks. The comparison of the performance analysis with some chaosbased hash functions of the literature and with standard hash functions make the proposed hash functions suitable for data integrity, message authentication, and digital signature applications.

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“…As given in Table 9, the proposed algorithm has a mean changed bit number B = 63.99 and mean changed probability P = 49.99 that are significantly close to the ideal values of 64 bits and 50 %, respectively, which indicate our algorithm has a strong capability for confusion and diffusion. As given in Table 9, the proposed algorithm has ideal values of B and P, and lower standard deviations of B and P, except for Xiao's [20,21,48], Wang's [40], Kanso's [49] and Teh's [50] algorithms. Based on these results, our algorithm shows good statistical performance.…”

Section: Comparison With Other Hash Algorithmsmentioning

confidence: 96%

Chaotic hash function based on the dynamic S-Box with variable parameters

Xia

2016

Nonlinear Dyn

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We present a chaotic hash function based on the dynamic S-Box with variable parameters in this paper. More specifically, we first exploit the piecewise linear chaotic map to obtain four initial buffers and an initial hash value. Then, we divide a randomly chosen message into message blocks and assign the four buffers and current message block to a transfer function to produce variable parameters and initial values of the PWLCM and logistic map for constructing a dynamic S-Box, which is then used for updating the four buffers. After all the message blocks are processed, the final hash value is generated by cascading the buffers and then applying XOR operation with the last hash value. Finally, we conduct performance evaluation on the proposed hash algorithm in terms of sensitivity, confusion and diffusion properties, collision resistances, speed analysis, randomness tests, and comparison with other algorithms, and the results demonstrate that the proposed algorithm has good statistical properties, strong collision resistances and better performance compared with other schemes.

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Parallel chaotic hash function based on the shuffle-exchange network

Cited by 62 publications

References 25 publications

GPUs and chaos: a new true random number generator

GPUs and chaos: a new true random number generator

Design and security analysis of two robust keyed hash functions based on chaotic neural networks

Chaotic hash function based on the dynamic S-Box with variable parameters

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