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

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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書誌詳細
主要な著者: Lapina, M. A., Лапина, М. А., Shiriaev, E. M., Ширяев, Е. М., Babenko, M. G., Бабенко, М. Г.
フォーマット: Статья
言語:English
出版事項: Pleiades Publishing 2024
主題:
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
オンライン･アクセス:https://dspace.ncfu.ru/handle/123456789/29339
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要約: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.

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