User-Defined Privacy Preserving Data Sharing for Connected Autonomous Vehicles Utilizing Edge Computing
Tianyu Bai, Qing Yang, Song Fu · 2023
In this paper, we present PRECISE, a novel privacy preserving data sharing framework for connected autonomous vehicles (CAVs). PRECISE allows users to define the objects or parts that they wish to protect privacy before sharing data with other vehicles. It leverages secure segmentation and inpainting technologies to protect sensitive data of vehicles. PRECISE explores the edges to offload resource-intensive deep learning workloads. To ensure data privacy in the processing on edge, PRECISE leverages additive secret sharing theory to define secure functions for deep neural networks (DNNs). Two secure DNN models, Secure SegNet and Secure Context Encoder, are introduced, along with detailed explanations of how to develop secure CNN layers and the secure functions used in building these layers. We have implemented a prototype of PRECISE and evaluated its performance. The experimental results demonstrate that PRECISE is lightweight, achieving secure segmentation in 3.47 seconds and secure inpainting in 0.99 seconds. The inference outputs from PRECISE remain the same as those from the original DNNs, while data privacy is protected. To the best of our knowledge, PRECISE is the first of its kind to provide user-defined privacy protection for sensor data sharing among CAVs.