Safe Storage of Distributed Double-Carbon Power Data Based on DBN Neural Network
Changyou Jiang, Fei Zhang, Manxue Li, Kuo Wang, Dashan Du, Ting‐Chang Chang · 2024
The long access time of private and tamper resistant data storage affects the effectiveness of data storage. For this purpose, a secure storage method for distributed dual carbon power data based on DBN neural network was designed. Generate renewable polynomial commitments for secure storage of power data, generate appropriate algebraic structures in the form of bilinear maps, and store power data together with the public and private key pairs of polynomial commitments. By using the DBN model, all nodes of the neural network are effectively connected in a “fully connected” form, thereby achieving secure storage of distributed dual carbon power data. Comparative experiments have shown that this method has good storage performance and can be applied in practical life.