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Feistel-Inspired Scrambling Improves the Quality of Linear Congruential Generators

Title: Feistel-Inspired Scrambling Improves the Quality of Linear Congruential Generators.
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Name(s): Aljahdali, Asia Othman, author
Mascagni, Michael, professor directing dissertation
Duke, D. W. (Dennis W.), university representative
Srinivasan, Ashok (Professor of Computer Science), committee member
van Engelen, Robert, committee member
Florida State University, degree granting institution
College of Arts and Sciences, degree granting college
Department of Computer Science, degree granting department
Type of Resource: text
Genre: Text
Doctoral Thesis
Issuance: monographic
Date Issued: 2017
Publisher: Florida State University
Place of Publication: Tallahassee, Florida
Physical Form: computer
online resource
Extent: 1 online resource (167 pages)
Language(s): English
Abstract/Description: Pseudorandom number generators (PRNGs) are an essential tool in many areas, including simulation studies of stochastic processes, modeling, randomized algorithms, and games. The performance of any 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 TestU01, DIEHARD, the tests from the SPRNG package, and a set of tests designed to evaluate bit sequences developed at NIST. TestU01 provides batteries of test that are sets of the mentioned suites. The predefined batteries are SmallCrush (10 tests, 16 p-values) that runs quickly, Crush (96 tests, 187 p-values) and BigCrush (106 tests, 2254 p-values) batteries that take longer to run. Most pseudorandom generators use recursion to produce sequences of numbers that appear to be random. The linear congruential generator is one of the well-known pseudorandom generators, the next number in the random sequences is determined by the previous one. The recurrences start with a value called the seed. Each time a recurrence starts with the same seed the same sequence is produced. This thesis develops a new pseudorandom number generation scheme that produces random sequences with good statistical properties via scrambling linear congruential generators. The scrambling technique is based on a simplified version of Feistel network, which is a symmetric structure used in the construction of cryptographic block ciphers. The proposed research seeks to improve the quality of the linear congruential generators’ output streams and to break up the regularities existing in the generators.
Identifier: FSU_SUMMER2017_Aljahdali_fsu_0071E_13941 (IID)
Submitted Note: A Dissertation submitted to the Department of Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Degree Awarded: Summer Semester 2017.
Date of Defense: May 4, 2017.
Keywords: Feistel network, Linear congruential generators, Pseudorandom numbers
Bibliography Note: Includes bibliographical references.
Advisory Committee: Michael Mascagni, Professor Directing Dissertation; Dennis Duke, University Representative; Ashok Srinivasan, Committee Member; Robert van Engelen, Committee Member.
Subject(s): Computer science
Persistent Link to This Record: http://purl.flvc.org/fsu/fd/FSU_SUMMER2017_Aljahdali_fsu_0071E_13941
Owner Institution: FSU

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Aljahdali, A. O. (2017). Feistel-Inspired Scrambling Improves the Quality of Linear Congruential Generators. Retrieved from http://purl.flvc.org/fsu/fd/FSU_SUMMER2017_Aljahdali_fsu_0071E_13941