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Wave: A decentralized authorization framework with transitive delegation

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dc.contributor.authorAndersen, Michael P.-
dc.contributor.authorKumar, Sam-
dc.contributor.authorAbdelBaky, Moustafa-
dc.contributor.authorFierro, Gabe-
dc.contributor.authorKolb, John-
dc.contributor.authorKim, Hyung-Sin-
dc.contributor.authorCuller, David E.-
dc.contributor.authorPopa, Raluca Ada-
dc.date.accessioned2024-05-08T01:12:56Z-
dc.date.available2024-05-08T01:12:56Z-
dc.date.created2024-04-16-
dc.date.created2024-04-16-
dc.date.created2024-04-16-
dc.date.issued2019-
dc.identifier.citationProceedings of the 28th USENIX Security Symposium, pp.1375-1392-
dc.identifier.urihttps://hdl.handle.net/10371/201066-
dc.description.abstractMost deployed authorization systems rely on a central trusted service whose compromise can lead to the breach of millions of user accounts and permissions. We present WAVE, an authorization framework offering decentralized trust: no central services can modify or see permissions and any participant can delegate a portion of their permissions autonomously. To achieve this goal, WAVE adopts an expressive authorization model, enforces it cryptographically, protects permissions via a novel encryption protocol while enabling discovery of permissions, and stores them in an untrusted scalable storage solution. WAVE provides competitive performance to traditional authorization systems relying on central trust. It is an open-source artifact and has been used for two years for controlling 800 IoT devices.-
dc.language영어-
dc.publisherUSENIX Association-
dc.titleWave: A decentralized authorization framework with transitive delegation-
dc.typeArticle-
dc.citation.journaltitleProceedings of the 28th USENIX Security Symposium-
dc.identifier.wosid000509775000079-
dc.identifier.scopusid2-s2.0-85076380805-
dc.citation.endpage1392-
dc.citation.startpage1375-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorKim, Hyung-Sin-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
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  • Graduate School of Data Science
Research Area Distributed machine learning, Edge, Mobile AI

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