Research on Privacy Protection Methods for Virtual Power Plant Users Based on Data Precision Desensitization

With the rapid development of the energy Internet, virtual power plants have emerged as an innovative model for the integrated distribution of energy resources. However, during the transmission of information from power plants, there is a risk of users' personal information being leaked. To address this issue, this paper proposes a user-sensitive data protection scheme for virtual power plants, which includes data desensitization, a desensitization algorithm, and an optimized sensitive data scanning approach. The proposed scheme employs a replacement algorithm within the data desensitization process to encrypt and protect sensitive information, such as users' names and electricity consumption behaviors. Simulation analysis demonstrates that this algorithm not only retains key characteristics of electricity consumption data—allowing for meaningful insights during data analysis—but also effectively prevents the exposure of specific values. This enhances the privacy of users' electricity consumption data while achieving dynamic desensitization. Overall, this approach satisfies the data analysis requirements within the virtual power plant framework while effectively safeguarding user privacy, providing a practical solution for balancing rational power data usage with privacy protection.