Privacy-Preserving Outsourcing Learning for Connected Autonomous Vehicles: Challenges, Solutions, and Perspectives

Although data sharing and fusion between connected autonomous vehicles (CAVs) can effectively enhance environment awareness and improve driving safety, it has to face severe challenges of privacy disclosure. Outsourcing encrypted data to edge servers for data analysis and model learning can alleviate this issue without imposing additional computing load on CAVs. In this article, we propose a privacy-preserving outsourcing learning (PPOL) framework based on lightweight additive secret sharing (ASS). Firstly, we propose a privacy-preserving outsourcing object detection method over secretly shared image and point cloud data. Secondly, we construct a privacy-preserving outsourcing depth estimation model over fused stereo image shares, aiming to provide a feasible solution for outsourcing data fusion and learning. Finally, we point out open issues under the PPOL framework, and give research perspectives in aspects of privacy-preserving data fusion for multi-frame, multi-modal and multi-view data, as well as privacy-preserving model optimization.