Power Quality Data Security Transmission and Protection Technology of Urban Energy Internet Combining Generation Countermeasure Network and Differential Privacy

Abstract

In transmitting power-quality data, traditional encryption is insufficient and easily intercepted and tampered with. Directly transmitting real data can easily expose sensitive information, and plain text data without noise is prone to leakage, posing high privacy risks. To solve the security and privacy protection problems of power quality data transmission in the urban energy Internet, this article combined the Generative Adversarial Network (GAN) and Differential Privacy (DP) technologies to enhance data privacy protection. GAN can generate virtual data instead of real data, reducing the exposure of sensitive information, while utilizing DP to add Gaussian noise and Laplacian noise. The generated data can be compared with the original data, and various data attack methods have been simulated to test the data protection and privacy protection capabilities of the model. The model can be compared and analyzed in depth with other traditional data security transmission and protection models. The experiment shows that the GAN combined with DP technology model studied in this article achieved an accuracy of 95.6% between generated data and real data while preserving data privacy. The privacy budget of the model was 0.12, the noise intensity was 1.2, and the data risk probability was 0.04%. All of them are superior to other traditional models such as GAN, DP, federated generative adversarial network, and Local Differential Privacy Generative Adversarial Network (LDP-GAN). The model of GAN and DP combined in this article can effectively reduce the privacy risk of power quality data in urban energy Internet, and can resist multiple data attacks.