Secure Deep Learning in Defense in Deep-Learning-as-a-Service Computing Systems in Digital Twins-Reference-Cited by-同舟云学术

Secure Deep Learning in Defense in Deep-Learning-as-a-Service Computing Systems in Digital Twins

Published:2023 Issue: Volume: Page:1-1
ISSN:0018-9340
Container-title:IEEE Transactions on Computers
language:
Short-container-title:IEEE Trans. Comput.

Author:

Lv Zhihan¹,Chen Dongliang²,Cao Bin³,Song Houbing⁴,Lv Haibin⁵

Affiliation:

1. School of Data Science and Software Engineering, Qingdao University, 12593 Qingdao, Shandong, China, Qingdao (e-mail: lvzhihan@gmail.com)

2. School of Data Science and Software Engineering, Qingdao University, 12593 Qingdao, Shandong, China, (e-mail: 936418030@qq.com)

3. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, 12606 Tianjin, Tianjin, China, 300401 (e-mail: caobin@scse.hebut.edu.cn)

4. Department of Electrical Engineering and Computer Science, Embry-Riddle Aeronautical University, 2830 Daytona Beach, Florida, United States, 32114-3900 (e-mail: h.song@ieee.org)

5. North China Sea Offshore Engineering Survey Institute, Ministry Of Natural Resources North Sea Bureau, Qingdao, Shandong, China, (e-mail: lvhaibinsoa@gmail.com)

Funder

NationalNaturalScienceFoundationofChina

Key Research and Development Plan - Major Scientific and Technological Innovation Projects of ShanDong Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/12/4358213/10195839.pdf?arnumber=10195839

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