Data security sharing method for smart grid data middle platform using combined keys in trusted controlled environments

Smart grid data hubs face multifaceted security challenges during data sharing due to the intertwined nature of personal privacy and commercial secrets within grid data. These challenges include external attacks, unauthorized internal access, and insufficient transmission encryption. Existing single-dimensional security mechanisms struggle to address these issues. Therefore, this study investigates a secure data sharing method for smart grid data hubs that employs combined keys within a trusted controlled environment. For data sharing within the smart grid data middle platform, a blockchain-based secure sharing model is constructed involving five types of entities, including certificate authorities and data owners. This model standardizes the entire process from initialization and data preprocessing to publication and retrieval. During the sharing management process, a trustworthiness calculation model is designed. Trust certificates are constructed by combining user misconduct records with service interaction frequency to compute user violation probability and trustworthiness, thereby dynamically allocating user access permissions. A combined key generation method is employed, where dynamic keys are generated based on a key seed matrix combined with timestamps and random numbers. These keys, when integrated with the AES-128 symmetric encryption algorithm, enable data encryption and decryption. This approach achieves secure data sharing within the smart grid data middle platform using combined keys under a trusted and controlled environment. Experimental results demonstrate that the trusted computing model does not impact the platform's routine operations during non-access request states, effectively intercepts unauthorized access, and maintains stable coupling even with increasing concurrent users. The combination of combined keys and the AES-128 algorithm ensures that encrypted data exhibits uniform character frequency distribution, significantly enhancing resistance to analysis and safeguarding the confidentiality and availability of shared data.