Parallel Hash Algorithm Based on Cellular Automata and Stochastic Diffusion Model

Parallel Hash Algorithm Based on Cellular Automata and Stochastic Diffusion Model | 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 Article Parallel Hash Algorithm Based on Cellular Automata and Stochastic Diffusion Model Yijun Yang, Huan Wan, Xiaohu Yan, Ming Zhao, Jianhua Zeng, Bin Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4648031/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 3 You are reading this latest preprint version Abstract The development of a cryptographic hash algorithm is a crucial task due to its numerous practical applications, such as digital signatures, blockchain, and distributed systems. Constructing a novel and efficient hash algorithm that meets the high security requirements is a challenging endeavor. This study introduces a cryptographic parallel hash algorithm based on cellular automata and a stochastic diffusion model, referred to as PCASD. The article delves into the rules of cellular automata, classifies 88 types of equivalent class rules, and utilizes random chaotic rules to generate keys for iterative processes. The stochastic diffusion model optimizes parameters to achieve optimal safety performance indicators. The parallel iteration structure allows for simultaneous execution of different branches, ultimately resulting in a hash value. The experimental results demonstrate that the proposed parallel hash algorithm outperforms popular hash functions in terms of randomness, avalanche, information entropy, collision resistance, and efficiency, indicating its practical feasibility. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Information technology cryptographic hash algorithm stochastic diffusion model cellular automata collision-resistance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editor invited by journal 02 Jul, 2024 Submission checks completed at journal 02 Jul, 2024 First submitted to journal 27 Jun, 2024 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. 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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-4648031","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":323935984,"identity":"0d67b5ed-d18c-4367-9147-67afdaeb4bc1","order_by":0,"name":"Yijun Yang","email":"","orcid":"","institution":"Shenzhen Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Yang","suffix":""},{"id":323935985,"identity":"bab4382c-f8e7-4b87-98b9-935cd28056c5","order_by":1,"name":"Huan Wan","email":"","orcid":"","institution":"Shenzhen Polytechnic 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