Rahma Hammedi

The operation of Connected and Autonomous Vehicles (CAVs) is primarily driven by the continuous exchange of data from various sources, including in-vehicle sensors, neighbouring vehicles, and roadside infrastructure. This continuous data exchange results in the accumulation of large volumes of data with dimensionality, which is essential for accurate decision-making in autonomous driving functions. Shared data often encompasses highly sensitive data such as precise vehicular location, driver identification and behavioural patterns. As a result, there is an increasing public concern over the privacy implications associated with the extensive data exchanges. In this context, this research investigates access control mechanisms that ensure privacy and trust in CAVs. We propose a Reputation-based Hyperledger Fabric (RepHLF) framework, a novel privacy-preserving architecture that integrates across-channel Hyperledger Fabric blockchain with a dynamic, multi-metric reputation model for CAV environments. The reputation mechanism evaluates vehicles based on three parameters: behavioural integrity, legitimacy, and historical communication reliability. Each parameter evolves using exponential decay functions to reflect temporal relevance. Data access is subsequently restricted to trusted vehicles only, according to their computed reputation index. The performance of RepHLF is evaluated in terms of accuracy, latency, memory usage, privacy loss, and communication overhead using Hyperledger Caliper. Simulation results demonstrate ultra-low average latency of approximately 201 ms, minimal communication cost of 2.8 KB per transaction, Throughput of 4.99 TPS and high learning accuracy of 99.94%, while maintaining a bounded privacy loss of 3.12%. The integrated reputation mechanism within the consensus algorithm further enhances network reliability by dynamically identifying untrusted vehicles and restricting their transaction requests. Further analysis of the proposed model, RepHLF, against existing reputation-based models demonstrates superior dynamic trust evolution and robust privacy preservation.

Advancements in the Connected and Autonomous Vehicles (CAVs) industry are revolutionizing modern transportation through advanced automation levels and connectivity capabilities. While autonomous vehicles can operate using onboard sensors alone, the integration of Vehicle-to-Everything (V2X) communication is vital for enabling seamless connectivity and cooperative decision-making. However, the increasing exchange of traffic and sensor data introduces critical privacy challenges, necessitating robust and scalable privacy-preserving mechanisms to ensure user trust and compliance with data protection regulations. The inherently dynamic nature of CAV environments, characterized by high mobility, short-duration connections, and frequent handovers, further complicates the design of effective privacy models. In this context, this paper investigates the evolving data privacy risks associated with CAV systems. It critically reviews existing privacy-preserving approaches and identifies their limitations in dynamic vehicular contexts. In particular, the paper explores the role of Federated Learning, permissioned blockchain and Software-Defined Networking (SDN) as enabling technologies for privacy preservation in CAVs. The analysis concludes with targeted recommendations for optimizing these frameworks to enhance privacy resilience in next-generation intelligent transportation systems.