Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12258/12083
Title: Homomorphic encryption methods review
Authors: Kucherov, N. N.
Кучеров, Н. Н.
Deryabin, M. A.
Дерябин, М. А.
Babenko, M. G.
Бабенко, М. Г.
Keywords: Distributed data storages;Homomorphic encryptions;Machine learning;Modular arithmetic;Neural networks;Cryptography
Issue Date: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: Kucherov, N.N., Deryabin, M.A., Babenko, M.G. Homomorphic Encryption Methods Review // Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020. - 2020. - Номер статьи 9039110. - Pages 370-373
Series/Report no.: Proceedings of the 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering, EIConRus 2020
Abstract: Today, cloud technology continues to evolve. Using cloud services allows you to get financial benefits Still, many companies and users are in no hurry to transfer their infrastructure to the cloud due to incompletely resolved problems related to the security of data storage and processing. Since in the case of storing and processing open data, a cloud provider gets access to the data, and an attacker can also gain by hacking an account. In the case of encrypted data transmission to the cloud, confidentiality will be preserved only in data storage tasks, since data processing will require a decryption task. The use of homomorphic ciphers allows the processing of encrypted data without violating their privacy. Homomorphic encryption is actively beginning to be used in machine learning tasks to transfer and ensure the confidentiality of resource-intensive operations for training a neural network in the cloud. The article offers a review and comparison of existing methods of homomorphic encryption for machine learning tasks
URI: http://hdl.handle.net/20.500.12258/12083
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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