A Privacy‐Preserving Threat Intelligence Model for Secure Healthcare Data Sharing in the Cloud

ABSTRACT In the contemporary healthcare landscape, secure and efficient data sharing is paramount, especially when utilizing cloud‐based platforms. The advent of cloud computing has revolutionized healthcare data sharing, offering unparalleled accessibility and scalability. However, the inherent risks associated with data breaches and privacy violations pose significant challenges, necessitating robust security measures. In such scenarios, the integration of threat intelligence with privacy‐preserving techniques becomes imperative to safeguard sensitive healthcare information. This research introduces a novel algorithm, FedGANet, alongside an integrated Privacy‐Preserving Threat Intelligence Model (FedGAN‐PPTIM), developed to strengthen secure healthcare data exchange within cloud and IoMT environments. FedGANet enhances traditional security paradigms by jointly leveraging Generative Adversarial Networks (GANs) to synthesize realistic threat scenarios and Federated Learning (FL) to enable decentralized model training without exposing sensitive patient data. The model further aligns with interoperability considerations, supporting seamless integration into diverse clinical ecosystems. The proposed FedGAN‐PPTIM framework is extensively compared with established privacy‐preserving and threat intelligence approaches across multiple evaluation metrics, including privacy leakage, threat detection rate, false positive rate, and communication overhead. The simulation analysis demonstrates that FedGANet outperforms existing methods, significantly reducing privacy leakage and communication overhead while maintaining high threat detection rates and low false positive rates. These results underscore the efficacy of FedGANet in addressing privacy and security challenges in healthcare data sharing. This article is categorized under: Technologies > Cloud Computing Technologies > Artificial Intelligence Commercial, Legal, and Ethical Issues > Security and Privacy