Leveraging A New GAN-based Transformer with ECDH Crypto-system for Enhancing Energy Theft Detection in Smart Grid Article Swipe
Yang Yang
,
Xun Yuan
,
Arwa Alromih
,
Aryan Mohammadi Pasikhani
,
Prosanta Gope
,
Biplab Sikdar
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.18023
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.18023
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.18023
- https://arxiv.org/pdf/2411.18023
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404990634
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404990634Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.18023Digital Object Identifier
- Title
-
Leveraging A New GAN-based Transformer with ECDH Crypto-system for Enhancing Energy Theft Detection in Smart GridWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-27Full publication date if available
- Authors
-
Yang Yang, Xun Yuan, Arwa Alromih, Aryan Mohammadi Pasikhani, Prosanta Gope, Biplab SikdarList of authors in order
- Landing page
-
https://arxiv.org/abs/2411.18023Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2411.18023Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2411.18023Direct OA link when available
- Concepts
-
Transformer, Grid, Embedded system, Smart grid, Computer science, Computer security, Electrical engineering, Engineering, Geography, Voltage, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| citation_normalized_percentile |