Privacy-Preserving Object Detection with Secure Convolutional Neural Networks for Vehicular Edge Computing
Description:
Article discusses how with the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as object detection using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge servers. The authors aim to address the privacy problem by protecting both vehicles' sensor data and the detection results.
Date:
October 31, 2022
Creator:
Bai, Tianyu; Fu, Song & Yang, Qing
Partner:
UNT College of Engineering