High-Speed Convolution Core Architecture for Privacy-Preserving Neural Networks
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| 出版年: | Programming and Computer Software vol. 50, no. 6 (Dec 2024), p. 417 |
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| 第一著者: | |
| その他の著者: | , , |
| 出版事項: |
Springer Nature B.V.
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| 主題: | |
| オンライン･アクセス: | Citation/Abstract Full Text Full Text - PDF |
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| 抄録: | 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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| ISSN: | 0361-7688 1608-3261 |
| DOI: | 10.1134/S0361768824700282 |
| ソース: | Advanced Technologies & Aerospace Database |