The 6G wireless network aims to forge a new spectrum, high technical standards of high time and phase synchronization accuracy, and 100% geographic coverage to connect trillions of devices flexibly and efficiently in the future. However, as connectivity increases and applications become novel, it is a challenge to ensure the privacy and security of networks and applications. Blockchain is seen as a promising technology that can improve efficiency, reduce costs, mitigate security, and privacy threats, and establish a trusted data-sharing environment. This article presents a trusted framework based on blockchain technology from the perspective of how to build a trusted software-defined content delivery network. As the peer node of the blockchain, the software-defined network (SDN) controller establishes trust between different regions and a wide range of participants, realizing peer autonomy and flexible business orchestration. The two main purposes of the architecture are to enhance the security of network communications and establish trust relationships between entities in different domains. It includes trusted communication based on routing sandbox, service choreography based on blockchain, proxy server selection strategy based on model predictive control (MPC), and optimization consensus based on practical Byzantine fault tolerance. Some simulation experiments verify the effectiveness of the theoretical method.

As a new trusted data sharing pattern with privacy protection, the integration mechanism of blockchain and Federated Learning has attracted extensive attention. Generally, this mechanism uses blockchain technology to supervise the original data and calculation results, which ignores the supervision of the Federated Learning model and computing process. Therefore, we introduce the concepts of the sandbox and state channel to construct a new data privacy sharing paradigm via Blockchain and Federated Learning. Under this paradigm, we use state channel to connect Blockchain and Federated Learning. And state channel is used to create a “trusted sandbox” to instantiate Federated Learning tasks in the trustless edge computing environment. Meanwhile, we also mainly solve problems about data privacy sharing in Federated Learning and system performance degradation caused by data quality. The simulation results show that the proposed method has better performance and efficiency than the traditional data sharing method.