Data sharing in privacy preserving intelligent transportation system based on blockchain and low-rank gradient compression

Intelligent Transportation Systems (ITS) utilize advanced technologies such as the Internet of Things (IoT), Cloud Computing (CC), and Artificial Intelligence (AI) to enhance transportation services. A critical aspect of ITS is secure and efficient data sharing, where ensuring data privacy remains a primary research focus. This study proposes a novel data sharing scheme that integrates federated learning with blockchain technology. The scheme employs low-rank gradient compression and utilizes blockchain for secure training data storage, enabling participants to update a global model collaboratively without a central server while managing data access via smart contracts. Furthermore, it incorporates a lightweight blockchain consensus mechanism for efficient global model aggregation from roadside nodes, meeting the stringent efficiency and real-time demands of connected vehicle environments. Experimental results demonstrate the scheme’s feasibility and superior performance, showing a 10% reduction in communication overhead compared to existing methods, while maintaining robustness against malicious vehicle activities.