scholarly journals Statistical independence of linear congruential pseudo-random numbers

1976 ◽  
Vol 82 (6) ◽  
pp. 927-930 ◽  
Author(s):  
Harald Niederreiter
Keyword(s):  
Random Numbers ◽  
Statistical Independence ◽  
Linear Congruential

Feistel-inspired scrambling improves the quality of linear congruential generators

2017 ◽  
Vol 23 (2) ◽  
Author(s):  
Asia Aljahdali ◽  
Michael Mascagni
Keyword(s):  
Randomized Algorithms ◽  
Statistical Properties ◽  
Pseudorandom Number ◽  
Random Numbers ◽  
Pseudorandom Sequences ◽  
Linear Congruential Generators ◽  
Pseudorandom Number Generation ◽  
Linear Congruential ◽  
Test Suites

AbstractGenerating pseudorandom numbers is a prerequisite for many areas including Monte Carlo simulation and randomized algorithms. The performance of pseudorandom number generators (PRNGs) depends on the quality of the generated random sequences. They must be generated quickly and have good statistical properties. Several statistical test suites have been developed to evaluate a single stream of random numbers such as those from the TestU01 library, the DIEHARD test suite, the tests from the SPRNG package, and a set of tests designed to evaluate bit sequences developed at NIST. This paper presents a new pseudorandom number generation scheme that produces pseudorandom sequences with good statistical properties via a scrambling procedure motivated by cryptographic transformations. We will specifically apply this to a popular set of PRNGs called the Linear Congruential generators (LGCs). The scrambling technique is based on a simplified version of a Feistel network. The proposed method seeks to improve the quality of the LCGs output stream. We show that this Feistel-inspired scrambling technique breaks up the regularities that are known to exist in LCGs. The Feistel-inspired scrambling technique is modular, and can be applied to any 64-bit PRNG, and so we believe that it can serve as an inexpensive model for a scrambler that can be used with most PRNGs via post-processing.

scholarly journals Penerapan Kriptografi RSA Dalam Mengamankan File Teks Berbasis PHP

2018 ◽  
Vol 2 (1) ◽  
pp. 45
Author(s):  
Dicky Apdilah ◽  
Heru Swanda
Keyword(s):  
Data Storage ◽  
Prime Numbers ◽  
Public Key ◽  
Random Numbers ◽  
Public And Private ◽  
The Public ◽  
Human Needs ◽  
Private Key ◽  
Use Of Technology ◽  
Linear Congruential

Abstract - Along with the development of communication technology human needs in the use of technology are increasing, especially in data storage. One way to improve security for data is by using cryptographic methods. RSA Algorithm (Rivest Shamir Adleman) is one method in the branch of cryptography, where RSA is a type of asymmetric cryptography that uses 2 keys, namely public and private keys. The problem of increasing the security of the public key and private key in RSA (Rivest Shamir Adlema) is that the Linear Congruential Generator (LCG) method is needed, LCG is used to generate a set of random numbers to n, where a set of random numbers will be taken that have a number value prime. One method for generating prime numbers is The Sieve Of Eratosthenes algorithm, The Sieve Of Eratosthenes algorithm is a classic algorithm for determining all prime numbers until the n-number is specified. The way the The Sieve Of Eratosthenes method works is to eliminate numbers that are not prime numbers, resulting in a collection of prime numbers. The prime number generated by the The Sieve Of Eratosthenes algorithm will be used for the public key and private key in the RSA criterion. Keywords - RSA, LCG, The Sieve of Eratosthenes.

scholarly journals A Better Approach to Generating Random Numbers

2019 ◽  
Vol 8 (1) ◽  
pp. 29-35
Author(s):  
Nachandiya Nathan ◽  
Samaila Andrew Mamza
Keyword(s):  
Random Number ◽  
Random Number Generator ◽  
Online Games ◽  
Random Numbers ◽  
Number Generator ◽  
Acceptable Result ◽  
Mathematical Background ◽  
Linear Congruential Generator ◽  
Linear Congruential ◽  
Or Modelling

The term random number has been used by many scholars to explain the behaviour of a stochastic system. Many of such scholars with statistical or mathematical background view it as an organized set of numbers produced by a function in a numerical way in which the next number to be produced is unknown or unpredictable. This paper produced software that generates a sequence of random number and also compared the algorithm with the commonly used method of random number generator. The three most common methods selected were the Mid Square method, Fibonacci method and Linear Congruential Generator Method (LCG). The result shows that the LCG provides a more acceptable result in terms of speed, long cycle, uniformity and independence Applications of this random numbers can be seen in Monte Carlo simulations, simulation or modelling, password generation, cryptography and online games.

Penerapan Linear Congruential Method pada Aplikasi Ujian Berbantuan Komputer

2018 ◽  
Vol 1 ◽  
Author(s):  
Puji Rahayu Ningsih ◽  
Muhamad Afif Effindi
Keyword(s):  
Learning Outcomes ◽  
Student Learning Outcomes ◽  
Random Numbers ◽  
Pseudorandom Number Generator ◽  
Computer Based ◽  
Congruential Method ◽  
Written Test ◽  
Linear Congruential ◽  
Bloom Taxonomy ◽  
Randomization Method

In order to obtain information regarding student learning outcomes, the test is one way to go. Both test orally and written test. Along with the development of technology, the current written test is no longer done on paper but done using computer media. Computer-based test is one of the exam types applied in the National Exam and have been implemented in some schools in Indonesia. In this exam, it is possible for students to work on different packages of problems with their colleagues on the side. This is possible because of the problem randomization method. Nevertheless, it should be tested whether in the randomization of the problem has noticed the sequence of occurrences of questions which is still pay attention to the sequence as Bloom Taxonomy. This study focuses on applying the Linear Congruential Method, in order to generate a random problem from the database. The method called Pseudorandom Number Generator. In practice, the Linear Congruential Method method use variables a, c, and m as inputs for the occurrence of random numbers simultaneously. In this study, the numbers a and m are set first. While the variable c obtained from random numbers generated in the previous stage. The result of this research is the computer-based test which applies for random numbers.

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