Privacy and Trust in the Internet of Vehicles

Abstract

The Internet of Vehicles aims to fundamentally improve transportation by connecting vehicles, drivers, passengers, and service providers together. Several new services such as parking space identification, platooning and intersection control-to name just a few-are expected to improve traffic congestion, reduce pollution, and improve the efficiency, safety and logistics of transportation. Proposed end-user services, however, make extensive use of private information with little consideration for the impact on users and third parties (those individuals whose information is indirectly involved). This article provides the first comprehensive overview of privacy and trust issues in the Internet of Vehicles at the service level. Various concerns over privacy are formalised into four basic categories: privacy of personal information, trust, consent to provide information, and multi-party privacy. To help analyse services and to facilitate future research, the main relevant end-user services are taxonomised according to voluntary and involuntary information they require and produce. Finally, this work identifies several open research problems and highlights general approaches to address them. These especially relate to measuring the tradeoff between privacy and service functionality, automated consent negotiation, trust towards the IoV and its individual services, and identifying and resolving multi-party privacy conflicts.

A Comprehensive Survey of V2X Cybersecurity Mechanisms and Future Research Paths

“…Collaborative learning underpins local decisions and allows vehicles to exchange their locally trained model parameters. As discussed in Section IV.B, albeit FL approaches are gradually gaining momentum towards the realization of privacy-by-design V2X (e.g., anonymous data collection and/or retrieval [425]), they still rely on a server-client architecture, rendering them vulnerable to several attacks (e.g., due to faulty software, hardware invasions, and unreliable communication channel). In addition, locally trained ML models may suffer from data poisoning and adversarial manipulations (e.g., malicious samples deliberately crafting the model).…”

Blockchain‐enhanced trust management for mobile edge computing‐enabled intelligent vehicular collaboration in the 6G era

“…Some work has focused on vehicular applications with centralized trust mechanisms. 7,35 For example, Zhang et al 36 proposed centralized Software-Defined Vehicular Networks (SDVNs) considering trust management using deep reinforcement learning. They proved their mechanism could enhance the link quality via simulation.…”

“…However, all the above research only considered communication performance without security risks, thus lacking the security strategies against malicious attacks, which might cause serious traffic accidents in the worst cases. 7 Some researchers have focused on the significance of security. [8][9][10] They have proposed various security strategies for V2X communications using cryptography, 11,12 fuzzy and other traditional security considerations.…”

Service-Centric Heterogeneous Vehicular Network Modeling for Connected Traffic Environments

“…Driver privacy is a potential issue due to the growing advancement toward the connected-vehicle environment [ 26 ]. There are different types of privacy concerns in connected vehicle environments, including personal information privacy [ 27 ], location privacy [ 28 ], driving-data privacy [ 29 ], third-party privacy [ 30 ], and information sharing consent–related privacy [ 31 ].…”