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dc.contributor.authorLivesay, Neal-
dc.contributor.authorJonatan, Gilbert-
dc.contributor.authorMora, Evelio-
dc.contributor.authorShivdikar, Kaustubh-
dc.contributor.authorAgrawal, Rashmi-
dc.contributor.authorJoshi, Ajay-
dc.contributor.authorAbellán, José L.-
dc.contributor.authorKim, John-
dc.contributor.authorKaeli, David-
dc.contributor.otherFacultades, Departamentos, Servicios y Escuelas::Departamentos de la UMU::Ingeniería y Tecnología de Computadoreses
dc.date.accessioned2023-03-08T09:04:36Z-
dc.date.available2023-03-08T09:04:36Z-
dc.date.issued2023-03-14-
dc.identifier.citationIEEE Micro ( Volume: 43, Issue: 5, Sept.-Oct. 2023)es
dc.identifier.issn1937-4143-
dc.identifier.urihttp://hdl.handle.net/10201/129148-
dc.description© 2023. The authors. This manuscript version is made available under the CC-BY4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the accepted version of a Published Work that appeared in final form in IEEE Micro. To access the final edited and published work see https://doi.org/10.1109/MM.2023.3253052es
dc.description.abstractFully Homomorphic Encryption (FHE) is a rapidly developing technology that enables computation directly on encrypted data, making it a compelling solution for security in cloud-based systems. In addition, modern FHE schemes are believed to be resistant to quantum attacks. Although FHE offers unprecedented potential for security, current implementations suffer from prohibitively high latency. Finite field arithmetic operations, particularly the multiplication of high-degree polynomials, are key computational bottlenecks. The parallel processing capabilities provided by modern Graphical Processing Units (GPUs) make them compelling candidates to target these highly parallelizable workloads. In this article, we discuss methods to accelerate polynomial multiplication with GPUs, with the goal of making FHE practical.es
dc.formatapplication/pdfes
dc.format.extent9es
dc.languageenges
dc.publisherIEEE Computer Society-
dc.relationThis work was supported in part by the Institute for Experiential AI, the Harold Alfond Foundation, the NSF IUCRC Center for Hardware and Embedded Systems Security and Trust (CHEST), the RedHat Collaboratory, and project grant PID2020-112827GB-I00 funded by MCIN/AEI/10.13039/501100011033.es
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleAccelerating Finite Field Arithmetic for Homomorphic Encryption on GPUses
dc.typeinfo:eu-repo/semantics/articlees
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/10068510-
dc.identifier.doihttps://doi.org/10.1109/MM.2023.3253052-
Aparece en las colecciones:Artículos: Ingeniería y Tecnología de Computadores



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