Leveraging A New GAN-based Transformer with ECDH Crypto-system for Enhancing Energy Theft Detection in Smart Grid

• URL: http://arxiv.org/abs/2411.18023v1
• Date: Wed, 27 Nov 2024 03:41:38 GMT
• Title: Leveraging A New GAN-based Transformer with ECDH Crypto-system for Enhancing Energy Theft Detection in Smart Grid
• Authors: Yang Yang, Xun Yuan, Arwa Alromih, Aryan Mohammadi Pasikhani, Prosanta Gope, Biplab Sikdar,
• Abstract summary: Split-learning is a promising machine learning technique for identifying energy theft. Traditional split learning approaches are vulnerable to privacy leakage attacks. We propose a novel GAN-Transformer-based split learning framework in this paper.
• Score: 16.031989793237152
• License:
• Abstract: Detecting energy theft is vital for effectively managing power grids, as it ensures precise billing and prevents financial losses. Split-learning emerges as a promising decentralized machine learning technique for identifying energy theft while preserving user data confidentiality. Nevertheless, traditional split learning approaches are vulnerable to privacy leakage attacks, which significantly threaten data confidentiality. To address this challenge, we propose a novel GAN-Transformer-based split learning framework in this paper. This framework leverages the strengths of the transformer architecture, which is known for its capability to process long-range dependencies in energy consumption data. Thus, it enhances the accuracy of energy theft detection without compromising user privacy. A distinctive feature of our approach is the deployment of a novel mask-based method, marking a first in its field to effectively combat privacy leakage in split learning scenarios targeted at AI-enabled adversaries. This method protects sensitive information during the model's training phase. Our experimental evaluations indicate that the proposed framework not only achieves accuracy levels comparable to conventional methods but also significantly enhances privacy protection. The results underscore the potential of the GAN-Transformer split learning framework as an effective and secure tool in the domain of energy theft detection.

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