Verifiable Model Procurement for Industrial CPS Using Cryptographic Performance Attestation

Verifiable Model Procurement for Industrial CPS Using Cryptographic Performance Attestation | 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 Verifiable Model Procurement for Industrial CPS Using Cryptographic Performance Attestation Jay Bojič Burgos, Urban Sedlar, Matevž Pustišek This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6567437/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Integrating third-party Machine Learning (ML) models into industrial Operational Technology (OT) creates a procurement deadlock: operators cannot verify vendor performance claims without exposing sensitive operational data, while vendors refuse to reveal proprietary model weights before purchase, rendering traditional safeguards such as Non-Disclosure Agreements technically unenforceable. This paper introduces a framework combining Zero Knowledge Proofs (ZKPs) with smart contracts to enable trust-minimized, privacy-preserving competitive model procurement in Industrial Cyber-Physical Systems (ICPS). Our framework allows vendors to cryptographically prove that their model outperforms a legacy baseline without disclosing proprietary weights, a process we term cryptographic performance attestation . The on-chain workflow combines escrow-backed procurement, automated proof verification, and best-vendor selection with arbiter-based dispute resolution. We analyze three distinct ZKP workflow variations for industrial suitability and evaluate their performance on consumer-grade hardware, achieving proving times of approximately three seconds and sub-dollar on-chain verification costs under Layer-2 fee assumptions for the recommended single-proof variation. Results demonstrate the feasibility of pre-deployment model verification while identifying computational trade-offs of recursive proof aggregation. The entire verification phase operates offline with no impact on real-time OT control paths, bridging the IT/OT pre-transaction trust gap while deferring artifact deployment to existing OT tooling. Zero Knowledge Proofs Blockchain Machine Learning Artificial Intelligence Collaborative Systems Privacy-Preserving Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-6567437","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468312788,"identity":"7549638d-d9e4-48f5-8db0-c2c4d51fdc32","order_by":0,"name":"Jay Bojič Burgos","email":"data:image/png;base64,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","orcid":"","institution":"University of Ljubljana","correspondingAuthor":true,"prefix":"","firstName":"Jay","middleName":"Bojič","lastName":"Burgos","suffix":""},{"id":468312789,"identity":"94d17151-c9bd-4b8e-99e7-66659e988b4b","order_by":1,"name":"Urban Sedlar","email":"","orcid":"","institution":"University of Ljubljana","correspondingAuthor":false,"prefix":"","firstName":"Urban","middleName":"","lastName":"Sedlar","suffix":""},{"id":468312790,"identity":"ed9aabc1-05f7-44d0-9785-b7c5674b07fe","order_by":2,"name":"Matevž Pustišek","email":"","orcid":"","institution":"University of Ljubljana","correspondingAuthor":false,"prefix":"","firstName":"Matevž","middleName":"","lastName":"Pustišek","suffix":""}],"badges":[],"createdAt":"2025-04-30 19:08:04","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6567437/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-6567437/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104397792,"identity":"0ab5889c-cb5c-45ef-8bce-62e395a43dfa","added_by":"auto","created_at":"2026-03-11 11:56:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1073442,"visible":true,"origin":"","legend":"","description":"","filename":"verifprocurement.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6567437/v2_covered_5ef78c23-42ab-49a9-8301-9d05029c5de8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eVerifiable Model Procurement for Industrial CPS Using Cryptographic Performance Attestation\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Zero Knowledge Proofs, Blockchain, Machine Learning, Artificial Intelligence, Collaborative Systems, Privacy-Preserving","lastPublishedDoi":"10.21203/rs.3.rs-6567437/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6567437/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntegrating third-party Machine Learning (ML) models into industrial Operational Technology (OT) creates a procurement deadlock: operators cannot verify vendor performance claims without exposing sensitive operational data, while vendors refuse to reveal proprietary model weights before purchase, rendering traditional safeguards such as Non-Disclosure Agreements technically unenforceable. 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