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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Главные авторы:	Lapina, M. A., Лапина, М. А., Shiriaev, E. M., Ширяев, Е. М., Babenko, M. G., Бабенко, М. Г.
פורמט:	Статья
שפה:	English
יצא לאור:	Pleiades Publishing 2024
נושאים:	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
גישה מקוונת:	https://dspace.ncfu.ru/handle/123456789/29339
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!

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סיכום:	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.

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

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