Towards Privacy-Preserving and Secure Data Flow for Multimodal Transport: Overview, Limitations, Solutions and Directions

In recent years, the demand for multimodal transport has grown alongside global economic growth, making it a crucial component of modern logistics systems. However, multimodal transport faces challenges such as fragmented information, lack of standardization, and significant vulnerabilities in data privacy and security. This review offers a comprehensive analysis of privacy-preserving and secure data flow in multimodal transport, illustrating its architecture, limitations, and adaptive solutions. Firstly, the concept and significance of multimodal transport are explored, emphasizing its benefits and the pressing challenges in digitization and data security. Next, the integration of blockchain technology is examined, demonstrating its adaptability to multimodal transport for enhancing trust, data regulation, and seamless logistics processes, while the limitations of existing blockchain-based multimodal transport in ensuring privacy-preserving and secure interactions are discussed. Advanced solutions applicable to multimodal transport, including privacy-preserving sharing methods and robust authentication mechanisms, are reviewed as effective strategies to mitigate current challenges. Lastly, this review identifies key future research directions to foster the development of secure, efficient, and scalable multimodal transport systems.