Title
Survey on Fully Homomorphic Encryption, Theory, and ApplicationsAuthor
Author (from another institution)
xmlui.dri2xhtml.METS-1.0.item-contributorOtherinstitution
Technology Innovation Institute (TII), Abu Dhabihttps://ror.org/028zdr819
https://ror.org/042aqky30
Version
http://purl.org/coar/version/c_ab4af688f83e57aa
Rights
© 2022 IEEEAccess
http://purl.org/coar/access_right/c_abf2Publisher’s version
https://doi.org/10.1109/JPROC.2022.3205665Published at
Proceedings of the IEEE Vol. 110. N. 10. Pp. 1572-1609. October, 2022xmlui.dri2xhtml.METS-1.0.item-publicationfirstpage
1572xmlui.dri2xhtml.METS-1.0.item-publicationlastpage
1609Publisher
IEEEKeywords
Homomorphic encryption
neural networks
Cloud computing
Public key ... [+]
neural networks
Cloud computing
Public key ... [+]
Homomorphic encryption
neural networks
Cloud computing
Public key
Gaussian distribution
Privacy
Internet of Things [-]
neural networks
Cloud computing
Public key
Gaussian distribution
Privacy
Internet of Things [-]
Abstract
Data 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 nex ... [+]
Data 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. [-]
xmlui.dri2xhtml.METS-1.0.item-sponsorship
Gobierno Vasco-Eusko Jaurlaritzaxmlui.dri2xhtml.METS-1.0.item-projectID
info:eu-repo/grantAgreement/GV/Ikertalde Convocatoria 2022-2025/IT1676-22/CAPV/Grupo de sistemas inteligentes para sistemas industriales/Collections
- Articles - Engineering [644]