Performance Comparison of Linear Congruent Method and Fisher-Yates Shuffle for Data Randomization

Juniawan, Fransiskus Panca; Pradana, Harrizki Arie; Laurentinus, Laurentinus; Sylfania, Dwi Yuny

doi:10.1088/1742-6596/1196/1/012035

Cited by 15 publications

(10 citation statements)

References 6 publications

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“…The results are presented in Figure 3. , which expands the results obtained in [35]. It should be noted that PRS formed according to the proposed method is not cryptographically stable and can not be used in "pure" form in cryptographic transformations, for example, as a gamma for stream ciphers.…”

Section:  supporting

confidence: 65%

“…Figure 3: Graphs of dependence of speed of permutation generators on M value The speed of the developed generator exceeds the speed of the permutation generator using the Fisher-Yates algorithm for 125 M , which expands the results obtained in[35]. It should be noted that PRS formed according to the proposed method is not cryptographically stable and can not be used in "pure" form in cryptographic transformations, for example, as a gamma for stream ciphers.…”

supporting

confidence: 54%

See 1 more Smart Citation

Method for Generating Pseudorandom Sequence of Permutations Based on Linear Congruential Generator

Faure¹,

Fedorov²,

Myronets³

et al. 2022

Computer Modeling and Intelligent Systems

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Section:  supporting

confidence: 65%

supporting

confidence: 54%

Method for Generating Pseudorandom Sequence of Permutations Based on Linear Congruential Generator

Faure¹,

Fedorov²,

Myronets³

et al. 2022

Computer Modeling and Intelligent Systems

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“…The Fisher–Yates Shuffle (FYS) algorithm was used to randomly permute the columns of the PCI matrix. The algorithm has two attractive properties: permutations are unbiased, and the shuffling has a linear time complexity

[ 49 ]. The algorithm was used with values from the Tent map chaotic sequence as described in Algorithm 1.…”

Section: Proposed Sensing Matrixmentioning

confidence: 99%

An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks

Skosana

Abu‐Mahfouz

2023

Sensors

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A measurement matrix is essential to compressed sensing frameworks. The measurement matrix can establish the fidelity of a compressed signal, reduce the sampling rate demand, and enhance the stability and performance of the recovery algorithm. Choosing a suitable measurement matrix for Wireless Multimedia Sensor Networks (WMSNs) is demanding because there is a sensitive weighing of energy efficiency against image quality that must be performed. Many measurement matrices have been proposed to deliver low computational complexity or high image quality, but only some have achieved both, and even fewer have been proven beyond doubt. A Deterministic Partial Canonical Identity (DPCI) matrix is proposed that has the lowest sensing complexity of the leading energy-efficient sensing matrices while offering better image quality than the Gaussian measurement matrix. The simplest sensing matrix is the basis of the proposed matrix, where random numbers were replaced with a chaotic sequence, and the random permutation was replaced with random sample positions. The novel construction significantly reduces the computational complexity as well time complexity of the sensing matrix. The DPCI has lower recovery accuracy than other deterministic measurement matrices such as the Binary Permuted Block Diagonal (BPBD) and Deterministic Binary Block Diagonal (DBBD) but offers a lower construction cost than the BPBD and lower sensing cost than the DBBD. This matrix offers the best balance between energy efficiency and image quality for energy-sensitive applications.

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“…Call the relevant random number function 4. Output random numbers 𝜀𝜀 1 , 𝜀𝜀 2 , 𝜀𝜀 3 …that meet the conditions The random number function in algorithm 1 is generated based on linear congruence method (LCG) [32]. The general basic recursive formula of LCG is: 𝜑𝜑 𝑛𝑛+1 = (𝛼𝛼𝜑𝜑 𝑛𝑛 + 𝛽𝛽)𝑚𝑚𝑚𝑚𝑚𝑚(𝑀𝑀)…”

Section: Algorithm 1: Random Number Generated By Random Number Functionmentioning

confidence: 99%

BCsRNG: A Secure Random Number Generator Based on Blockchain

et al. 2022

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Random numbers are widely used in numerical computing, statistical simulation, random sampling, etc. At present, the mechanism for generating random numbers by computers is at risk of being attacked. In some cases, generated random numbers may be predicted. However, current RNGs used in blockchain are not sufficient enough to handle attacks. Therefore, it is necessary to improve the security of random numbers. This paper improves the random number generator and designs a secure random number generator based on blockchain (BCsRNG). In practice, an encapsulated function algorithm of the secure random number is developed with the smart contract. Moreover, an API is provided to facilitate input and output. The experimental results show that the random numbers generated by BCsRNG are difficult to decipher and have higher randomness and security.INDEX TERMS Random number, Blockchain, Smart contract, Security, Random number generator I. INTRODUCTION

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Performance Comparison of Linear Congruent Method and Fisher-Yates Shuffle for Data Randomization

Cited by 15 publications

References 6 publications

Method for Generating Pseudorandom Sequence of Permutations Based on Linear Congruential Generator

Method for Generating Pseudorandom Sequence of Permutations Based on Linear Congruential Generator

An Energy-Efficient Sensing Matrix for Wireless Multimedia Sensor Networks

BCsRNG: A Secure Random Number Generator Based on Blockchain

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