During the 14th Five-Year Plan period, China's urban rail transit market has exhibited steady growth, paralleled by increases in passenger volume and emerging safety challenges. The advent of national standards such as GB 51151 has heightened safety requirements, pressing the need for technological advancements in rail transit security systems. Traditional security systems suffer from isolated operations and inefficient information exchanges, necessitating additional human resources for management. We propose integrating blockchain technology to enhance trust and security across disparate systems. Additionally, the introduction of heterogeneous query blockchain middle-ware facilitates cross-chain data interoperability and advanced querying capabilities, further enriching our multimodal, fine-grained blockchain security management system that leverages Fabric's channel isolation for secondary permission control. This system not only ensures secure data transmission and storage but also addresses privacy and trust issues, enabling unified data handling and traceability across rail transit security platforms. The experiment demonstrated the efficacy of our work

The Internet of Vehicles (IoV) has become a research hotspot owing to the continuous enrichment and expansion of the industrial ecology. IoV data is complex due to heterogeneity and dynamic topology, posing challenges for traditional processing methods and limited onboard device capabilities. To address this, cloud computing is essential for constructing a high-performance IoV network with accurate machine learning, extracting latent value from data. Despite cloud advances, privacy concerns in data transmission and processing within IoV persist. This paper proposed a lightweight fully homomorphic encryption algorithm to address privacy. Notably, the proposed encryption algorithm can be reduced to the conjugacy search problem (CSP) under the standard model. Based on the algorithm, a model for processing encrypted data is established and implemented on a neural network for traffic data classification. The approach is compared against conventional methods in terms of complexity, efficiency, and security. Results unequivocally demonstrate comparable accuracy with original neural networks. In contrast to traditional homomorphic encryption, the proposed approach provides equivalent security with a substantial 100-fold increase in efficiency.