Achieving Differentially Private Location Privacy in Edge-Assistant Connected Vehicles

Abstract

Connected vehicles can provide safer and more satisfying services for drivers by using information sensing and sharing. However, current network architecture cannot support massive and real-time data transmissions due to the poor-quality wireless links. To provide real-time data processing and improve drivers' security, edge computing is regarded as a promising method to offer more efficient services by placing computing and storage resources at the network edge. In this paper, we will introduce the concept of edge-assistant connected vehicles and propose some promising applications to reduce the network traffic and provide real-time services with the help of massive edge nodes. Unlike in the traditional cloud-based connected vehicles, edge nodes are introduced to enable vehicles to obtain real-time and distributed processing services. Furthermore, considering the location privacy issue in the new architecture, we propose a novel differentially privacy-preserving location-based service usage framework deployed on the edge node, designed to provide an adjustable privacy protection solution to balance the utility and privacy. Finally, we conduct extensive experiments to verify the proposed framework.