mathrm^: A Privacy-Preserving Perception Framework for Building Vehicle-Edge Perception Networks Protecting Data Privacy

• Published in: 2023 32nd International Conference on Computer Communications and Networks (ICCCN) pp. 1 - 10
• Main Authors: Bai, Tianyu, Shao, Danyang, He, Ying, Fu, Song, Yang, Qing
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2023
• Subjects: Connected and Autonomous Vehicles; Cryptography; Data privacy; Deep Learning; Degradation; Edge Computing; Image edge detection; Perception; Privacy; Real-time systems; Servers; Vehicle Computing
• ISSN: 2637-9430
• DOI: 10.1109/ICCCN58024.2023.10230191

With the wider adoption of edge computing services, intelligent edge devices, and high-speed V2X communication, compute-intensive tasks for autonomous vehicles, such as perception using camera, LiDAR, and/or radar data, can be partially offloaded to road-side edge units. However, data privacy becomes a major concern for vehicular edge computing, as sensor data with sensitive information from vehicles can be observed and used by edge servers. We aim to address the privacy problem by protecting both vehicles' sensor data and the detection results. In this paper, we present a privacy preserving perception (\mathbf{P}^{3}) framework which provides a secure version of every commonly used layers in various perception CNN networks. They server as the building blocks to facilitate the construction of a privacy preserving CNN for any existing or future network. \mathbf{P}^{3} leverages the additive secret sharing theory to develop secure functions for perception networks. A vehicle's sensor data is split and encrypted into multiple secret shares, each of which is processed on an edge server by going through the secure layers of a detection network. The detection results can only be obtained by combining the partial results from the participating edge servers. We present two use cases where the secure layers in \mathbf{P}^{3} are used to build privacy preserving both single-stage and two-stage object detection CNNs. Experimental results indicate data privacy for vehicles is protected without comprising the detection accuracy and with a reasonable amount of performance degradation. To the best of our knowledge, this is the first work that provides a generic framework to ease the development of vehicle-edge perception networks protecting data privacy.