Research on Data Protection of Electric Power Marketing Based on Differential Privacy

In this paper, we proposed an improved electricity marketing data protection algorithm based on differential privacy, aiming to address several challenges faced by traditional differential privacy mechanisms in practical applications. Our solution introduces a dynamic privacy budget allocation mechanism, which can intelligently adjust the strength of privacy protection based on query characteristics, achieving a balance between data utility and privacy protection. In addition, we have designed a multi-layer privacy protection architecture that combines technologies such as homomorphic encryption and federated learning to construct a comprehensive data protection system, improving the flexibility and security of data processing. The algorithm also supports personalized privacy preference settings for users, optimizing noise addition strategies through deep learning technology to achieve more precise data utility protection. Meanwhile, we have developed real-time privacy impact assessment and verifiable privacy proof capabilities, increasing the transparency and credibility of the system. Finally, in order to address the privacy protection needs of cross domain data fusion, we propose a cross domain privacy coordination mechanism to ensure privacy consistency of data in different scenarios. The experimental results show that the algorithm significantly improves the availability and utility of data while ensuring user privacy, providing a more reliable and efficient data protection solution for power marketing data analysis.