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oai:ebiltegia.mondragon.edu:20.500.11984/59722024-04-08T08:37:22Zcom_20.500.11984_473col_20.500.11984_478

00925njm 22002777a 4500 dc Manzano Castro, Marc author 2022 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. 1558-2256 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=169004 https://hdl.handle.net/20.500.11984/5972 Homomorphic encryption neural networks Cloud computing Public key Gaussian distribution Privacy Internet of Things Survey on Fully Homomorphic Encryption, Theory, and Applications