Privacy-preserving multiparty power data sharing based on generative adversarial network

Zhenya Wang; Zesan Liu; Chenghua Fu; Aijun Wen; Min Zhang; Wenjuan Zhang

doi:10.1117/12.3024222

13 May 2024 Privacy-preserving multiparty power data sharing based on generative adversarial network

Zhenya Wang, Zesan Liu, Chenghua Fu, Aijun Wen, Min Zhang, Wenjuan Zhang

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 13159A9 (2024) https://doi.org/10.1117/12.3024222
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

With the rapid proliferation of smart grid technologies, a large amount of fine-grained power data records has been collected and stored by different parties (e.g., the power supply bureau). Pooling together the records held by the parties makes mining the data value, and therefore promises enhanced energy management and efficiency. Despite the benefits of sharing these data, it also raises concerns about data privacy and security. To this end, we present a novel approach for privacy-preserving cross-party power data sharing approach in light of the Generative Adversarial Network (named PowerGAN), which enables the involved parties to construct a shared dataset without compromising the privacy of these parties. In PowerGAN, a centralized curator is assigned a generator, while each party possesses a discriminator. The key idea of PowerGAN is to let data holders jointly train the generator held by the centralized server. In addition, to prevent the curator from inferring sensitive data about the parties, we designed a privacy preserving RMSProp (Root Mean Square Propagation) optimizer. Furthermore, we design a dynamic noise perturbation method, which dynamically tunes the noise to further promote the utility of the final shared data. Through comprehensive privacy analysis, we show that our PowerGAN approach provides strict privacy protection. Evaluations of real-world datasets show the effectiveness of PowerGAN in addressing the privacy concerns associated with multi-party power data sharing.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhenya Wang, Zesan Liu, Chenghua Fu, Aijun Wen, Min Zhang, and Wenjuan Zhang "Privacy-preserving multiparty power data sharing based on generative adversarial network", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 13159A9 (13 May 2024); https://doi.org/10.1117/12.3024222

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KEYWORDS

Education and training

Data privacy

Data modeling

Gallium nitride

Power supplies

Design

Mathematical optimization

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