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

Abstract

Multimodal transport plays a vital role in modern logistics by integrating multiple transportation modes to improve efficiency and reduce costs. However, challenges such as fragmented information, lack of standardized electronic documentation, as well as critical privacy, security, and trust vulnerabilities hinder its development. This review offers a comprehensive analysis of privacy-preserving and secure data flow in multimodal transport, illustrating its architecture, limitations, and adaptive solutions in cross-domain logistic collaboration. We highlight the prospects of blockchain technology, particularly consortium blockchains, in enhancing trust, data regulation, and cross-domain collaboration, while identifying current limitations in ensuring privacy-preserving and secure interactions. Advanced solutions, including privacy-preserving sharing and robust authentication methods adapted to cross-domain multimodal transport scenarios that incorporate blockchain, are reviewed to address these gaps. Finally, we outline future research directions aimed at building secure, scalable, and interoperable multimodal transport systems.