Toward 6G Networks: Architecture and Strategy for Information Security Transmission Based on Multi-Source Heterogeneous Data

In the context of 6G networks, efficient information transmission at high speeds with large data volumes to the cloud poses technical challenges, particularly related to data type heterogeneity and information security. This is especially critical when dealing with extensive data from diverse users, leading to issues in data utilization efficiency and security. To tackle these challenges, an innovative approach has been developed, employing the following data processing methods: <xref ref-type="disp-formula" rid="deqn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1)</xref> Users utilize the Mobile Edge Computing server to train the initial model, sign and upload it to the Blockchain. Due to constraints in data block size, off-chain storage is implemented using InterPlanetary File System. Additionally, differential privacy technology safeguards user privacy during this process; <xref ref-type="disp-formula" rid="deqn2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(2)</xref> Addressing the vast multi-source heterogeneous data in the 6G network, the Federated Transfer Learning mode is employed. This involves combining cross-user characteristics in different business formats after the data processing center acquires user data. Blockchain replaces the centralized aggregator in the federated learning system, enhancing the security of data transmission. By utilizing actual datasets from sources such as sandstorm, typhoon, and flood data, simulation results demonstrate that the proposed model significantly enhances data utilization efficiency, improves the security of data transmission, predicts disasters, and minimizes disaster losses.