Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework
Flight operations data play a central role in ensuring flight safety, optimizing operations, and driving innovation. However, these data have become a key target for cyber-attacks, and are especially vulnerable to property inference attacks. Aiming at property inference attacks in shared application...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/1/41 |
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| author | Jin Lei Weiyun Li Meng Yue Zhijun Wu |
| author_facet | Jin Lei Weiyun Li Meng Yue Zhijun Wu |
| author_sort | Jin Lei |
| collection | DOAJ |
| description | Flight operations data play a central role in ensuring flight safety, optimizing operations, and driving innovation. However, these data have become a key target for cyber-attacks, and are especially vulnerable to property inference attacks. Aiming at property inference attacks in shared application model training, we proposed FedMeta-CTGAN, a novel approach that leverages federated meta-learning and conditional tabular generative adversarial networks (CTGANs) to protect flight operations data. Motivated by the need for secure data sharing in aviation, as highlighted by the Federal Aviation Administration’s requirement for ADS-B Out equipment on aircraft to create a shared situational awareness environment, our method aims to prevent sensitive information leakage while maintaining model performance. FedMeta-CTGAN exploits the natural privacy-preserving properties of a two-stage update in meta-learning, using real data to train the CTGAN model and synthetic fake data as query data during meta-training. Comprehensive experiments using a real flight operation dataset demonstrate the effectiveness of our proposed method. FedMeta-CTGAN adapts quickly to unbalanced data, achieving a prediction accuracy of 96.33%, while reducing the attacker’s inference AUC score to 0.51 under property inference attacks. Our contribution lies in the development of a secure and efficient data-sharing solution for flight operations data, which has the potential to revolutionize the aviation industry. |
| format | Article |
| id | doaj-art-8ee6619fa88a494abbba6bcb892aa035 |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-8ee6619fa88a494abbba6bcb892aa0352025-01-24T13:15:35ZengMDPI AGAerospace2226-43102025-01-011214110.3390/aerospace12010041Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta FrameworkJin Lei0Weiyun Li1Meng Yue2Zhijun Wu3College of Safety Science and Engineering, Civil Aviation University of China, No. 2898 Jinbei Highway, Tianjin 300300, ChinaCollege of Safety Science and Engineering, Civil Aviation University of China, No. 2898 Jinbei Highway, Tianjin 300300, ChinaCollege of Safety Science and Engineering, Civil Aviation University of China, No. 2898 Jinbei Highway, Tianjin 300300, ChinaCollege of Safety Science and Engineering, Civil Aviation University of China, No. 2898 Jinbei Highway, Tianjin 300300, ChinaFlight operations data play a central role in ensuring flight safety, optimizing operations, and driving innovation. However, these data have become a key target for cyber-attacks, and are especially vulnerable to property inference attacks. Aiming at property inference attacks in shared application model training, we proposed FedMeta-CTGAN, a novel approach that leverages federated meta-learning and conditional tabular generative adversarial networks (CTGANs) to protect flight operations data. Motivated by the need for secure data sharing in aviation, as highlighted by the Federal Aviation Administration’s requirement for ADS-B Out equipment on aircraft to create a shared situational awareness environment, our method aims to prevent sensitive information leakage while maintaining model performance. FedMeta-CTGAN exploits the natural privacy-preserving properties of a two-stage update in meta-learning, using real data to train the CTGAN model and synthetic fake data as query data during meta-training. Comprehensive experiments using a real flight operation dataset demonstrate the effectiveness of our proposed method. FedMeta-CTGAN adapts quickly to unbalanced data, achieving a prediction accuracy of 96.33%, while reducing the attacker’s inference AUC score to 0.51 under property inference attacks. Our contribution lies in the development of a secure and efficient data-sharing solution for flight operations data, which has the potential to revolutionize the aviation industry.https://www.mdpi.com/2226-4310/12/1/41property inference attackfederated meta-learningflight operation dataprivacy preserving |
| spellingShingle | Jin Lei Weiyun Li Meng Yue Zhijun Wu Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework Aerospace property inference attack federated meta-learning flight operation data privacy preserving |
| title | Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework |
| title_full | Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework |
| title_fullStr | Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework |
| title_full_unstemmed | Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework |
| title_short | Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework |
| title_sort | defend against property inference attack for flight operations data sharing in fedmeta framework |
| topic | property inference attack federated meta-learning flight operation data privacy preserving |
| url | https://www.mdpi.com/2226-4310/12/1/41 |
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