An adversarial network reinforcement method for power data time series generation based on knowledge transfer and Rayleigh differential privacy

• Published in: Journal of physics. Conference series 2024-08, Vol.2814 (1), p.12053
• Main Authors: Zhai, Feng, Li, Baofeng, Liang, Xiaobing, Qin, Yu, Ji, Wen
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Electricity consumption; Generative adversarial networks; Knowledge management; Machine learning; Power; Privacy; Recurrent neural networks; Static characteristics; Time series
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/2814/1/012053

In order to solve the problem of sensitive information leakage of power data in machine learning and artificial intelligence processing, this paper proposes a method of civic-text data reinforcement based on time series generation adversarial network, which combines knowledge transfer and Rényi differential privacy protection framework to balance privacy and availability, while improving the accuracy of the model. First, the generative adversarial network generates training data that is similar to the real data but secure through adversarial learning, ensuring that even if the data is stolen, the original information cannot be inferred. Secondly, through the integration of recurrent neural network training and autoencoder, the dynamic and static characteristics of the power load data can be effectively captured, and the high dimension of the learning space can be reduced. In addition, knowledge distillation technology is used to guide the generator of training student models through a powerful teacher network of knowledge transfer, improving the diversity and quality of the generated data. Finally, from the four aspects of privacy, data similarity and diversity, and practicability, the practical application value and potential of the framework for electricity consumption data are comprehensively evaluated.