CRS: A Privacy-Preserving Two-Layered Distributed Machine Learning Framework for IoV

Abstract

Nowadays, vehicles can provide many valuable data (such as the videos recorded by dashcams) for analytical model building. Integrating vehicular ad-hoc networks with Internet of Things (IoT), Internet of Vehicles (IoV) has a promising future. In IoV, vehicles maintain their own communication, computing, and learning capabilities. Thus, instead of sending the data to a central server for model training, which leads to a high communication overhead, vehicles can train the data locally. However, it is still a challenge to preserve the privacy while keeping both the communication and computation overheads of vehicles acceptable. In this paper, we present a distributed machine learning framework with a two-layered architecture. The architecture uniquely involves vehicle clusters, road-side units, and a central server, which provides a basic guarantee to the vehicle privacy and also limits the overhead. By carefully adopting cryptographic tools and techniques, the framework has the following properties: 1) it preserves the privacy of the local inputs and model weight vectors to all parties; 2) it protects the identities and trajectories of vehicles; 3) packet loss is handled in the application layer; 4) the evaluation shows that it is lightweight for vehicles. Compared with other existing works, the proposed framework is more suitable for IoV.