Achieving Lightweight and Privacy-Preserving Object Detection for Connected Autonomous Vehicles-Reference-Cited by-同舟云学术

Achieving Lightweight and Privacy-Preserving Object Detection for Connected Autonomous Vehicles

Published:2023-02-01 Issue:3 Volume:10 Page:2314-2329
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Bi Renwan¹^ORCID,Xiong Jinbo¹^ORCID,Tian Youliang²^ORCID,Li Qi³^ORCID,Choo Kim-Kwang Raymond⁴^ORCID

Affiliation:

1. Fujian Provincial Key Laboratory of Network Security and Cryptology, College of Computer and Cyber Security, Fujian Normal University, Fuzhou, China

2. State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China

3. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

4. Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, TX, USA

Funder

National Natural Science Foundation of China

Henan Key Laboratory of Network Cryptography Technology

Natural Science Foundation of Fujian Province

Open Research Fund of Key Laboratory of Cryptography of Zhejiang Province

Natural Science Foundation of Jiangsu Higher Education Institutions of China

Cloud Technology Endowed Professorship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10024910/09913215.pdf?arnumber=9913215

Reference42 articles.

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5. FALCON: A Fourier Transform Based Approach for Fast and Secure Convolutional Neural Network Predictions

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