Design and Analysis of a Cryptographic Hash Function Incorporating Parallel Confusion and a Multi-Compression Architecture

Design and Analysis of a Cryptographic Hash Function Incorporating Parallel Confusion and a Multi-Compression Architecture | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Design and Analysis of a Cryptographic Hash Function Incorporating Parallel Confusion and a Multi-Compression Architecture Yijun Yang, Linlin Wang, Meileng Yuan, Bin Li, Zhuolin Zhong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4884979/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The cryptographic hash function stands as a cornerstone among the trio of essential cryptographic algorithms that are ubiquitously utilized across blockchain technology, digital signature applications, cloud storage solutions, and numerous other domains. Currently, a series of MD4-inspired hash functions, including RIPEMD, RIPEMD128, MD5, and SHA-1, have been critically evaluated and deemed insufficient in terms of security[ 10 – 13 ], thereby emphasizing the paramount importance of heightened vigilance towards safeguarding the integrity of cryptographic hash functions. Notably, the preponderance of prevalent hash functions relies heavily on inefficient serial architectures, posing limitations in terms of performance and scalability. To address these shortcomings, this paper introduces a groundbreaking cryptographic hash function, predicated on a parallel confusion and multi-compression structure (PCMCH). This innovative methodology innovatively fills the input data through a parallel confusion compression mechanism, concurrently executing multi-faceted confusion compression on each message block. Furthermore, it expedites message diffusion by meticulously tuning adaptable permutation parameters, enhancing both the speed and efficacy of the process. The exhaustive experimental analysis conducted underscores the exceptional security characteristics of the proposed hash function, including irregularity, the avalanche effect, high information entropy, and robust collision resistance. Moreover, its performance surpasses that of existing parallel hash functions, marking it as a promising contender that offers superior efficiency and security, thereby presenting a viable alternative for applications requiring heightened cryptographic safeguards. cryptographic hash function parallel confusion multi-compression collision-resistance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4884979","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338366321,"identity":"3bf3449c-e6a3-476b-8e8a-e90c40e0d191","order_by":0,"name":"Yijun Yang","email":"","orcid":"","institution":"Guangdong Hong Kong Macao Greater Bay Area Artificial Intelligence Research Institute, Shenzhen Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Yang","suffix":""},{"id":338366322,"identity":"3c740b61-e33a-4b24-8f77-14eb33840940","order_by":1,"name":"Linlin 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