A Verifiable Privacy-Preserving Cross-Chain Protocol for Trusted Vehicle Edge Computing

Abstract

Vehicle Edge Computing (VEC) has emerged as a crucial element in modern vehicular computing systems, enhancing data processing efficiency between vehicles and nearby infrastructure, reducing latency, and improving the overall performance of intelligent transportation systems. However, VEC faces challenges, such as data inequality and privacy concerns, which may impede accurate data processing and decision-making across various components (e.g., vehicles, traffic signals, and roadside units). Existing studies attempt to address these challenges by relying on centralized servers to process cross-vehicle data. However, this approach introduces vulnerabilities, including single points of failure and potential performance bottlenecks. Moreover, many current methods overlook the need for data verifiability alongside privacy and security, thus complicating the traceability of data sources in vehicular environments. In this paper, we propose a verifiable, privacy-preserving cross-vehicle protocol based on relay chains, utilizing blockchain's distributed ledger technology to facilitate transparent and secure information sharing among vehicle edge nodes. Through tamper-proof bookkeeping and automated smart contracts, the protocol significantly enhances the efficiency and security of VEC. The relay chain functions as the central framework, employing homomorphic encryption and distributed private key technology to enable confidential data sharing and verifiable access to business-critical information across nodes. This solution effectively tackles the pressing challenges of privacy protection, reliability, and data traceability within current VEC systems. To enhance practicality, the protocol adopts a non-iterative and lightweight design, enabling efficient data exchange and low-latency cross-chain interaction in heterogeneous VEC systems. We demonstrate the feasibility and effectiveness of our protocol through extensive experimental data supported by theoretical analysis. The results show that the proposed protocol achieves competitive performance in computation cost, encryption latency, and cross-chain throughput, especially under increasing key sizes and node densities, confirming its efficiency and scalability in real-world vehicular deployments.