{"title":"Parametrically perturbed logistic map - a new approach based on the least significant bits in the state variable’s representation","authors":"Madhu Sharma","doi":"10.1016/j.ins.2025.122737","DOIUrl":null,"url":null,"abstract":"<div><div>Cryptography involves controlled randomization and de-randomization of digital data. In the essential cryptographic infrastructure, the role of Pseudo Random Number Generators (PRNGs) becomes significant. The present work focuses on a new method of implementing PRNGs using chaotic maps. Using this approach, the classical chaotic maps, which otherwise are found to be cryptographically inadequate, can be enhanced to meet the necessary statistical requirements. In this new approach, one of the parameters of a chaotic map can be varied using the lower bits of the floating-point representation of the map’s state variable. This methodology is demonstrated using one of the most commonly discussed chaotic maps - the logistic map. The modified logistic map is shown to have excellent chaotic characteristics across the entire range of the only remaining parameter - the map’s initial state. The resulting chaotic map is named ’Parametrically Perturbed Logistic Map (PPLM)’. The PPLM is used to implement a new Pseudo Random Bit Generator (PRBG) - the PPLM-PRBG. An extensive set of simulations is carried out on the PPLM-PRBG. Using a standard set of parametric studies, including the ’NIST test suite’, the new PRBG is found to have excellent statistical and cryptographic properties.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"725 ","pages":"Article 122737"},"PeriodicalIF":6.8000,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0020025525008734","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Cryptography involves controlled randomization and de-randomization of digital data. In the essential cryptographic infrastructure, the role of Pseudo Random Number Generators (PRNGs) becomes significant. The present work focuses on a new method of implementing PRNGs using chaotic maps. Using this approach, the classical chaotic maps, which otherwise are found to be cryptographically inadequate, can be enhanced to meet the necessary statistical requirements. In this new approach, one of the parameters of a chaotic map can be varied using the lower bits of the floating-point representation of the map’s state variable. This methodology is demonstrated using one of the most commonly discussed chaotic maps - the logistic map. The modified logistic map is shown to have excellent chaotic characteristics across the entire range of the only remaining parameter - the map’s initial state. The resulting chaotic map is named ’Parametrically Perturbed Logistic Map (PPLM)’. The PPLM is used to implement a new Pseudo Random Bit Generator (PRBG) - the PPLM-PRBG. An extensive set of simulations is carried out on the PPLM-PRBG. Using a standard set of parametric studies, including the ’NIST test suite’, the new PRBG is found to have excellent statistical and cryptographic properties.
期刊介绍:
Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions.
Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.