High-Speed Convolution Core Architecture for Privacy-Preserving Neural Networks

Due to legal restrictions or restrictions related to companies' internal information policies, businesses often do not trust sensitive information to public cloud providers. One of the mechanisms to ensure the security of sensitive data in clouds is homomorphic encryption. Privacy-preserving ne...

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Main Authors:	Lapina, M. A., Лапина, М. А., Shiriaev, E. M., Ширяев, Е. М., Babenko, M. G., Бабенко, М. Г.
Format:	Статья
Language:	English
Published:	Pleiades Publishing 2024
Subjects:	Convolutional neural networks Spatial complexity Cryptography Differential privacy High Speed Ho-momorphic encryptions Homomorphic-encryptions Information policy Legal restriction Multiplication algorithms Scalar multiplication Privacy preserving Neural-networks Convolution
Online Access:	https://dspace.ncfu.ru/handle/123456789/29339
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id	ir-123456789-29339
record_format	dspace
spelling	ir-123456789-293392024-12-09T13:02:05Z High-Speed Convolution Core Architecture for Privacy-Preserving Neural Networks Lapina, M. A. Лапина, М. А. Shiriaev, E. M. Ширяев, Е. М. Babenko, M. G. Бабенко, М. Г. Convolutional neural networks Spatial complexity Cryptography Differential privacy High Speed Ho-momorphic encryptions Homomorphic-encryptions Information policy Legal restriction Multiplication algorithms Scalar multiplication Privacy preserving Neural-networks Convolution Due to legal restrictions or restrictions related to companies' internal information policies, businesses often do not trust sensitive information to public cloud providers. One of the mechanisms to ensure the security of sensitive data in clouds is homomorphic encryption. Privacy-preserving neural networks are used to design solutions that utilize neural networks under these conditions. They exploit the homomorphic encryption mechanism, thus enabling the security of commercial information in the cloud. The main deterrent to the use of privacy-preserving neural networks is the large computational and spatial complexity of the scalar multiplication algorithm, which is the basic algorithm for computing mathematical convolution. In this paper, we propose a scalar multiplication algorithm that reduces the spatial complexity from quadratic to linear, and reduces the computation time of scalar multiplication by a factor of 1.38. 2024-12-09T13:01:09Z 2024-12-09T13:01:09Z 2024 Статья Lapina, M.A., Shiriaev, E.M., Babenko, M.G., Istamov, I. High-Speed Convolution Core Architecture for Privacy-Preserving Neural Networks // Programming and Computer Software. - 2024. - 50 (6). - pp. 417-424. - DOI: 10.1134/S0361768824700282 https://dspace.ncfu.ru/handle/123456789/29339 en Programming and Computer Software application/pdf application/pdf Pleiades Publishing
institution	СКФУ
collection	Репозиторий
language	English
topic	Convolutional neural networks Spatial complexity Cryptography Differential privacy High Speed Ho-momorphic encryptions Homomorphic-encryptions Information policy Legal restriction Multiplication algorithms Scalar multiplication Privacy preserving Neural-networks Convolution
spellingShingle	Convolutional neural networks Spatial complexity Cryptography Differential privacy High Speed Ho-momorphic encryptions Homomorphic-encryptions Information policy Legal restriction Multiplication algorithms Scalar multiplication Privacy preserving Neural-networks Convolution Lapina, M. A. Лапина, М. А. Shiriaev, E. M. Ширяев, Е. М. Babenko, M. G. Бабенко, М. Г. High-Speed Convolution Core Architecture for Privacy-Preserving Neural Networks
description	Due to legal restrictions or restrictions related to companies' internal information policies, businesses often do not trust sensitive information to public cloud providers. One of the mechanisms to ensure the security of sensitive data in clouds is homomorphic encryption. Privacy-preserving neural networks are used to design solutions that utilize neural networks under these conditions. They exploit the homomorphic encryption mechanism, thus enabling the security of commercial information in the cloud. The main deterrent to the use of privacy-preserving neural networks is the large computational and spatial complexity of the scalar multiplication algorithm, which is the basic algorithm for computing mathematical convolution. In this paper, we propose a scalar multiplication algorithm that reduces the spatial complexity from quadratic to linear, and reduces the computation time of scalar multiplication by a factor of 1.38.
format	Статья
author	Lapina, M. A. Лапина, М. А. Shiriaev, E. M. Ширяев, Е. М. Babenko, M. G. Бабенко, М. Г.
author_facet	Lapina, M. A. Лапина, М. А. Shiriaev, E. M. Ширяев, Е. М. Babenko, M. G. Бабенко, М. Г.
author_sort	Lapina, M. A.
title	High-Speed Convolution Core Architecture for Privacy-Preserving Neural Networks
title_short	High-Speed Convolution Core Architecture for Privacy-Preserving Neural Networks
title_full	High-Speed Convolution Core Architecture for Privacy-Preserving Neural Networks
title_fullStr	High-Speed Convolution Core Architecture for Privacy-Preserving Neural Networks
title_full_unstemmed	High-Speed Convolution Core Architecture for Privacy-Preserving Neural Networks
title_sort	high-speed convolution core architecture for privacy-preserving neural networks
publisher	Pleiades Publishing
publishDate	2024
url	https://dspace.ncfu.ru/handle/123456789/29339
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High-Speed Convolution Core Architecture for Privacy-Preserving Neural Networks

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