scholarly journals A Secure Random Number Generator with Immunity and Propagation Characteristics for Cryptography Functions

2021 ◽  
Vol 11 (17) ◽  
pp. 8073
Author(s):  
Rahul Saha ◽  
Ganesan Geetha ◽  
Gulshan Kumar ◽  
William J. Buchanan ◽  
Tai-hoon Kim
Keyword(s):  
Symmetric Functions ◽  
Random Number ◽  
Random Number Generator ◽  
Functional Requirements ◽  
Function Generator ◽  
Random Number Generators ◽  
Long Run ◽  
Cryptographic Algorithms ◽  
Block Based ◽  
Propagation Feature

Cryptographic algorithms and functions should possess some of the important functional requirements such as: non-linearity, resiliency, propagation and immunity. Several previous studies were executed to analyze these characteristics of the cryptographic functions specifically for Boolean and symmetric functions. Randomness is a requirement in present cryptographic algorithms and therefore, Symmetric Random Function Generator (SRFG) has been developed. In this paper, we have analysed SRFG based on propagation feature and immunity. Moreover, NIST recommended statistical suite has been tested on SRFG outputs. The test values show that SRFG possess some of the useful randomness properties for cryptographic applications such as individual frequency in a sequence and block-based frequency, long run of sequences, oscillations from 0 to 1 or vice-versa, patterns of bits, gap bits between two patterns, and overlapping block bits. We also analyze the comparison of SRFG and some existing random number generators. We observe that SRFG is efficient for cryptographic operations in terms of propagation and immunity features.

scholarly journals TRNGs from Pre-Formed ReRAM Arrays

Cryptography ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 8
Author(s):  
Bertrand Cambou ◽  
Donald Telesca ◽  
Sareh Assiri ◽  
Michael Garrett ◽  
Saloni Jain ◽  
...  
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Random Access ◽  
Random Number Generators ◽  
Cryptographic Algorithms ◽  
Cryptographic Keys ◽  
Low Levels ◽  
Stochastic Properties ◽  
Pseudo Random Number Generator ◽  
Number Of Cells

Schemes generating cryptographic keys from arrays of pre-formed Resistive Random Access (ReRAM) cells, called memristors, can also be used for the design of fast true random number generators (TRNG’s) of exceptional quality, while consuming low levels of electric power. Natural randomness is formed in the large stochastic cell-to-cell variations in resistance values at low injected currents in the pre-formed range. The proposed TRNG scheme can be designed with three interconnected blocks: (i) a pseudo-random number generator that acts as an extended output function to generate a stream of addresses pointing randomly at the array of ReRAM cells; (ii) a method to read the resistance values of these cells with a low injected current, and to convert the values into a stream of random bits; and, if needed, (iii) a method to further enhance the randomness of this stream such as mathematical, Boolean, and cryptographic algorithms. The natural stochastic properties of the ReRAM cells in the pre-forming range, at low currents, have been analyzed and demonstrated by measuring a statistically significant number of cells. Various implementations of the TRNGs with ReRAM arrays are presented in this paper.

SRAM Based Random Number Generator for Non-Repeating Pattern Generation

2014 ◽  
Vol 573 ◽  
pp. 181-186 ◽  
Author(s):  
G.P. Ramesh ◽  
A. Rajan
Keyword(s):  
Random Number ◽  
Statistical Tests ◽  
Random Number Generator ◽  
Random Number Generation ◽  
Pattern Generation ◽  
Storage Device ◽  
Number Generator ◽  
Random Number Generators ◽  
Field Programmable ◽  
The Look

—Field-programmable gate array (FPGA) optimized random number generators (RNGs) are more resource-efficient than software-optimized RNGs because they can take advantage of bitwise operations and FPGA-specific features. A random number generator (RNG) is a computational or physical device designed to generate a sequence of numbers or symbols that lack any pattern, i.e. appear random. The many applications of randomness have led to the development of several different methods for generating random data. Several computational methods for random number generation exist, but often fall short of the goal of true randomness though they may meet, with varying success, some of the statistical tests for randomness intended to measure how unpredictable their results are (that is, to what degree their patterns are discernible).LUT-SR Family of Uniform Random Number Generators are able to handle randomness only based on seeds that is loaded in the look up table. To make random generation efficient, we propose new approach based on SRAM storage device.Keywords: RNG, LFSR, SRAM

scholarly journals Delay-Based True Random Number Generator in Sub-Nanomillimeter IoT Devices

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 817
Author(s):  
Maulana Randa ◽  
Mohammad Samie ◽  
Ian K. Jennions
Keyword(s):  
Random Number ◽  
Promising Result ◽  
Physical Phenomenon ◽  
Random Number Generator ◽  
Ring Oscillator ◽  
Random Number Generators ◽  
Propagation Delays ◽  
Field Programmable ◽  
Iot Devices ◽  
Non Gaussian

True Random Number Generators (TRNGs) use physical phenomenon as their source of randomness. In electronics, one of the most popular structures to build a TRNG is constructed based on the circuits that form propagation delays, such as a ring oscillator, shift register, and routing paths. This type of TRNG has been well-researched within the current technology of electronics. However, in the future, where electronics will use sub-nano millimeter (nm) technology, the components become smaller and work on near-threshold voltage (NTV). This condition has an effect on the timing-critical circuit, as the distribution of the process variation becomes non-gaussian. Therefore, there is an urge to assess the behavior of the current delay-based TRNG system in sub-nm technology. In this paper, a model of TRNG implementation in sub-nm technology was created through the use of a specific Look-Up Table (LUT) in the Field-Programmable Gate Array (FPGA), known as SRL16E. The characterization of the TRNG was presented and it shows a promising result, in that the delay-based TRNG will work properly, with some constraints in sub-nm technology.

scholarly journals A new computer-controlled platform for ADC-based true random number generator and its applications

2019 ◽  
pp. 847-860 ◽  
Author(s):  
SELÇUK COŞKUN ◽  
İHSAN PEHLİVAN ◽  
AKİF AKGÜL ◽  
BİLAL GÜREVİN
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Digital Data ◽  
Pseudorandom Number Generator ◽  
Number Generator ◽  
Random Number Generators ◽  
True Random Number Generator ◽  
Sample Application ◽  
Computer Controlled ◽  
Entropy Source

The basis of encryption techniques is random number generators (RNGs). The application areas of cryptology are increasing in number due to continuously developing technology, so the need for RNGs is increasing rapidly, too. RNGs can be divided into two categories as pseudorandom number generator (PRNGs) and true random number generator (TRNGs). TRNGs are systems that use unpredictable and uncontrollable entropy sources and generate random numbers. During the design of TRNGs, while analog signals belonging to the used entropy sources are being converted to digital data, generally comparators, flip-flops, Schmitt triggers, and ADCs are used. In this study, a computer-controlled new and flexible platform to find the most appropriate system parameters in ADC-based TRNG designs is designed and realized. As a sample application with this new platform, six different TRNGs that use three different outputs of Zhongtang, which is a continuous time chaotic system, as an entropy source are designed. Random number series generated with the six designed TRNGs are put through the NIST800–22 test, which has the internationally highest standards, and they pass all tests. With the help of the new platform designed, ADC-based high-quality TRNGs can be developed fast and also without the need for expertise. The platform has been designed to decide which entropy source and parameter are better by comparing them before complex embedded TRNG designs. In addition, this platform can be used for educational purposes to explain how to work an ADC-based TRNG. That is why it can be utilized as an experiment set in engineering education, as well.

scholarly journals A Lightweight BCH Code Corrector of TRNG with Measurable Dependence

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Hojoong Park ◽  
Yongjin Yeom ◽  
Ju-Sung Kang
Keyword(s):  
Linear Code ◽  
Random Number ◽  
Random Number Generator ◽  
The Other ◽  
Bch Code ◽  
Compression Rate ◽  
Number Generator ◽  
Von Neumann ◽  
Other Hand ◽  
Cryptographic Algorithms

We propose a new lightweight BCH code corrector of the random number generator such that the bitwise dependence of the output value is controllable. The proposed corrector is applicable to a lightweight environment and the degree of dependence among the output bits of the corrector is adjustable depending on the bias of the input bits. Hitherto, most correctors using a linear code are studied on the direction of reducing the bias among the output bits, where the biased input bits are independent. On the other hand, the output bits of a linear code corrector are inherently not independent even though the input bits are independent. However, there are no results dealing with the independence of the output bits. The well-known von Neumann corrector has an inefficient compression rate and the length of output bits is nondeterministic. Since the heavy cryptographic algorithms are used in the NIST’s conditioning component to reduce the bias of input bits, it is not appropriate in a lightweight environment. Thus we have concentrated on the linear code corrector and obtained the lightweight BCH code corrector with measurable dependence among the output bits as well as the bias. Moreover, we provide some simulations to examine our results.

scholarly journals Design of Pseudo Random Number Generator using Linear Feedback Shift Register

2019 ◽  
Vol 9 (2) ◽  
pp. 1956-1965 ◽  
Keyword(s):  
Information Technology ◽  
Random Number ◽  
Random Number Generator ◽  
Shift Register ◽  
Linear Feedback Shift Register ◽  
Linear Feedback ◽  
Number Generator ◽  
Cryptographic Algorithms ◽  
Feedback Shift Register ◽  
Pseudo Random Number Generator

Nowadays security has become a great concern in the field of computer science and information technology. In order to protect data from unintended users and to achieve a desirable level of security, several cryptographic algorithms based on various technology have been proposed. Linear Feedback Shift Register (LFSR) may play an important role in the design of such cryptographic algorithms. LFSR based cryptographic algorithms are often lightweight in nature and are more suitable for resource constraining devices. In this paper we present a detailed analysis of LFSR and design of LFSR to implement cryptographic algorithms.

scholarly journals A novel pseudo-random number generator for IoT based on a coupled map lattice system using the generalised symmetric map

2022 ◽  
Vol 4 (2) ◽  
Author(s):  
Unsub Zia ◽  
Mark McCartney ◽  
Bryan Scotney ◽  
Jorge Martinez ◽  
Ali Sajjad
Keyword(s):  
Random Number ◽  
Chaotic Map ◽  
Random Number Generator ◽  
Building Blocks ◽  
Coupling Factor ◽  
Coupled Map Lattices ◽  
Random Number Generators ◽  
Random Sequences ◽  
Pseudo Random Number ◽  
Iot Devices

AbstractPseudo-random number generators (PRNGs) are one of the building blocks of cryptographic methods and therefore, new and improved PRNGs are continuously developed. In this study, a novel method to generate pseudo-random sequences using coupled map lattices is presented. Chaotic maps only show their chaotic behaviour for a specified range of control parameters, what can restrict their application in cryptography. In this work, generalised symmetric maps with adaptive control parameter are presented. This novel idea allows the user to choose any symmetric chaotic map, while ensuring that the output is a stream of independent and random sequences. Furthermore, to increase the complexity of the generated sequences, a lattice-based structure where every local map is linked to its neighbouring node via coupling factor has been used. The dynamic behaviour and randomness of the proposed system has been studied using Kolmogorov–Sinai entropy, bifurcation diagrams and the NIST statistical suite for randomness. Experimental results show that the proposed PRNG provides a large key space, generates pseudo-random sequences and is computationally suitable for IoT devices.

scholarly journals Low-Entropy Stochastic Processes for Generating k-Distributed and Normal Sequences, and the Relationship of These Processes with Random Number Generators †

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 838
Author(s):  
Boris Ryabko
Keyword(s):  
Stochastic Processes ◽  
Random Number ◽  
Infinite Sequence ◽  
Statistical Properties ◽  
Random Number Generators ◽  
Long Run ◽  
Relationship Of ◽  
Low Entropy ◽  
The Relationship

An infinite sequence x 1 x 2 . . . of letters from some alphabet { 0 , 1 , . . . , b - 1 } , b ≥ 2 , is called k-distributed ( k ≥ 1 ) if any k-letter block of successive digits appears with the frequency b - k in the long run. The sequence is called normal (or ∞-distributed) if it is k-distributed for any k ≥ 1 . We describe two classes of low-entropy processes that with probability 1 generate either k-distributed sequences or ∞-distributed sequences. Then, we show how those processes can be used for building random number generators whose outputs are either k-distributed or ∞-distributed. Thus, these generators have statistical properties that are mathematically proven.

scholarly journals Pseudo-Random Number Generator Based on Logistic Chaotic System

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 960 ◽  
Author(s):  
Luyao Wang ◽  
Hai Cheng
Keyword(s):  
Chaotic System ◽  
Random Number ◽  
Chaotic Behavior ◽  
Statistical Tests ◽  
Random Sequence ◽  
Random Number Generator ◽  
Statistical Characteristics ◽  
Random Number Generators ◽  
System Parameters ◽  
Pseudo Random Number

In recent years, a chaotic system is considered as an important pseudo-random source to pseudo-random number generators (PRNGs). This paper proposes a PRNG based on a modified logistic chaotic system. This chaotic system with fixed system parameters is convergent and its chaotic behavior is analyzed and proved. In order to improve the complexity and randomness of modified PRNGs, the chaotic system parameter denoted by floating point numbers generated by the chaotic system is confused and rearranged to increase its key space and reduce the possibility of an exhaustive attack. It is hard to speculate on the pseudo-random number by chaotic behavior because there is no statistical characteristics and infer the pseudo-random number generated by chaotic behavior. The system parameters of the next chaotic system are related to the chaotic values generated by the previous ones, which makes the PRNG generate enough results. By confusing and rearranging the output sequence, the system parameters of the previous time cannot be gotten from the next time which ensures the security. The analysis shows that the pseudo-random sequence generated by this method has perfect randomness, cryptographic properties and can pass the statistical tests.

Workflow Optimization of Raster Image Processor for Digital Press

2011 ◽  
Vol 101-102 ◽  
pp. 1069-1073
Author(s):  
Hua Wei Duan ◽  
Guang Xue Chen
Keyword(s):  
Cell Size ◽  
Random Number ◽  
Random Number Generator ◽  
Random Number Generators ◽  
Image Processor ◽  
Workflow Optimization ◽  
Raster Image ◽  
Congruential Generator ◽  
Linear Congruential ◽  
Digital Press

Raster image processor is decisive in modern digital printing machines. Three random number generators were examined for their applicabilities to raster image processor. Results show that random number generator should be carefully selected in terms of the screen cell size adopted: linear congruential generator (LCG) is the best for the screen cell size of 8 × 8 or 16 × 16; multiplicative congruential generator (MCG) is most desirable when the screen cell size equals to 6 × 6 or 12 × 12; in the case of 24 × 24 screen cell size, either LCG or MCG is optimum. The workflow of raster image processor was optimized according to our results. These findings are helpful to improve the performance of digital press that is equipped with raster image processor.

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