Defend Against Property Inference Attack for Flight Operations Data Sharing in FedMeta Framework - Jin Lei; Weiyun Li; Meng Yue; Zhijun Wu (Aerospace)
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.