Please use this identifier to cite or link to this item: https://dspace.ncfu.ru/handle/123456789/28755
Title: Analytical Review of Confidential Artificial Intelligence: Methods and Algorithms for Deployment in Cloud Computing
Authors: Shiriaev, E. M.
Ширяев, Е. М.
Nazarov, A. S.
Назаров, А. С.
Kucherov, N. N.
Кучеров, Н. Н.
Babenko, M. G.
Бабенко, М. Г.
Keywords: Artificial intelligence;Secret sharing scheme;Cloud computing;Homomorphic encryption;Neural network;Residue number system (RNS)
Issue Date: 2024
Publisher: Pleiades Publishing
Citation: Shiriaev, E.M., Nazarov, A.S., Kucherov, N.N., Babenko, M.G. Analytical Review of Confidential Artificial Intelligence: Methods and Algorithms for Deployment in Cloud Computing // Programming and Computer Software. - 2024. - 50 (4). - pp. 304-314. - DOI: 10.1134/S0361768824700117
Series/Report no.: Programming and Computer Software
Abstract: The technologies of artificial intelligence and cloud computing systems have recently been actively developed and implemented. In this regard, the issue of their joint use, which has been topical for several years, has become more acute. The problem of data privacy preservation in cloud computing acquired the status of critical long before the necessity of their joint use with artificial intelligence, which made it even more complicated. This paper presents an overview of both the artificial intelligence and cloud computing techniques themselves, as well as methods to ensure data privacy. The review considers methods that utilize differentiated privacy; secret sharing schemes; homomorphic encryption; and hybrid methods. The conducted research has shown that each considered method has its pros and cons outlined in the paper, but there is no universal solution. It was found that theoretical models of hybrid methods based on secret sharing schemes and fully homomorphic encryption can significantly improve the confidentiality of data processing using artificial intelligence.
URI: https://dspace.ncfu.ru/handle/123456789/28755
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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