A Data Protection Method for the Electricity Business Environment Based on Differential Privacy and Federal Incentive Mechanisms

Xu Zhou, Hongshan Luo, Simin Chen and Yuling He ()
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Xu Zhou: Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518048, China
Hongshan Luo: Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518048, China
Simin Chen: Shenzhen Power Supply Bureau Co., Ltd., Shenzhen 518048, China
Yuling He: Department of Mechanical Engineering, North China Electric Power University, Baoding 071003, China

Energies, 2025, vol. 18, issue 13, 1-28

Abstract: In the development process of the power industry, accurately assessing the level of development of the electricity business environment is of great significance. However, traditional evaluation systems have limitations, with the issue of “data silos” being prominent, and user privacy under federated learning is also at risk. This paper proposes a federated learning-based data protection method for the electricity business environment to address these challenges. Based on the World Bank’s B-READY framework, this paper constructs an electricity business environment evaluation system containing nine indicators, focusing on three aspects: electricity regulations, public services, and operational efficiency. The indicators are weighted using the Sequence Relation and Entropy Weight Method. To address the issue of sensitive data protection, we first use federated learning technology to build a distributed modeling framework, ensuring that raw data never leaves the local environment during the collaborative modeling process. Next, we embed a differential privacy mechanism in the model parameter transmission stage, encrypting the model parameters by adding controlled noise. Finally, an incentive mechanism based on contribution quantification is implemented to encourage participation from all parties. This paper conducts experiments using the data of Shenzhen City, Guangdong Province. Compared with the FNN model and the SVR model, the MLP model reduces MAE by 78.9% and 94.12%, respectively, and increases R 2 by 37.95% and 55.62%, respectively. The superiority of the method proposed in this paper has been proved.

Keywords: electricity business environment; differential privacy; federated learning; data protection; combined weighting or composite weighting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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