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dc.contributor.authorManzano Castro, Marc
dc.contributor.otherMarcolla, Chiara
dc.contributor.otherSucasas, Victor
dc.contributor.otherBassoli, Riccardo
dc.contributor.otherFitzek, Frank H. P.
dc.contributor.otherAaraj, Najwa
dc.date.accessioned2023-01-27T12:25:40Z
dc.date.available2023-01-27T12:25:40Z
dc.date.issued2022
dc.identifier.issn1558-2256en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=169004en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5972
dc.description.abstractData privacy concerns are increasing significantly in the context of the Internet of Things, cloud services, edge computing, artificial intelligence applications, and other applications enabled by next-generation networks. Homomorphic encryption addresses privacy challenges by enabling multiple operations to be performed on encrypted messages without decryption. This article comprehensively addresses homomorphic encryption from both theoretical and practical perspectives. This article delves into the mathematical foundations required to understand fully homomorphic encryption ( FHE ). It consequently covers design fundamentals and security properties of FHE and describes the main FHE schemes based on various mathematical problems. On a more practical level, this article presents a view on privacy-preserving machine learning using homomorphic encryption and then surveys FHE at length from an engineering angle, covering the potential application of FHE in fog computing and cloud computing services. It also provides a comprehensive analysis of existing state-of-the-art FHE libraries and tools, implemented in software and hardware, and the performance thereof.en
dc.description.sponsorshipGobierno Vasco-Eusko Jaurlaritzaes
dc.description.sponsorshipComisión Europeaes
dc.description.sponsorshipGobierno de Alemaniaes
dc.language.isoengen
dc.publisherIEEEen
dc.rights© 2022 IEEEen
dc.subjectHomomorphic encryptionen
dc.subjectneural networksen
dc.subjectCloud computingen
dc.subjectPublic keyen
dc.subjectGaussian distributionen
dc.subjectPrivacyen
dc.subjectInternet of Thingsen
dc.titleSurvey on Fully Homomorphic Encryption, Theory, and Applicationsen
dc.typeinfo:eu-repo/semantics/articleen
dcterms.accessRightsinfo:eu-repo/semantics/embargoedAccessen
dcterms.sourceProceedings of the IEEEen
dc.description.versioninfo:eu-repo/semantics/acceptedVersionen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.description.publicationfirstpage1572en
local.description.publicationlastpage1609en
local.identifier.doihttps://doi.org/10.1109/JPROC.2022.3205665en
local.relation.projectIDinfo:eu-repo/grantAgreement/GV/Ikertalde Convocatoria 2022-2025/IT1676-22/CAPV/Grupo de sistemas inteligentes para sistemas industriales/en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/101015956/EU/A flagship for B5G/6G vision and intelligent fabric of technology enablers connecting human, physical, and digital worlds/Hexa-Xen
local.relation.projectIDinfo:eu-repo/grantAgreement/Gobierno de Alemania/Excellence Strategy—EXC2050/390696704/DE/Cluster of Excellence “Centre for Tactile Internet with Human-in-the-Loop/CeTIen
local.embargo.enddate2024-10-31
local.contributor.otherinstitutionTechnology Innovation Institute (TII), Abu Dhabien
local.contributor.otherinstitutionhttps://ror.org/028zdr819en
local.contributor.otherinstitutionhttps://ror.org/042aqky30de
local.source.detailsVol. 110. N. 10. Pp. 1572-1609. October, 2022en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore


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