A robust approach for privacy data protection: IoT security assurance using generative adversarial imitation learning

In this article, starting from the privacy data protection mechanism in the Industrial Internet of Things (IIoT) scenario, we proposed a method based on generative adversarial imitation learning (GAIL) to discover the privacy data security risks in IIoT by training privacy protection agents using a large amount of expert data on privacy protection. Finally, our proposed method is validated by relevant simulation experiments, and the results show that our proposed method has wide generalizability and reliability to obtain the maximum payoff of the agents and thus, reduce the risk of data security leakage.

Recommendation cited: C. Huang, S. Chen, Y. Zhang, Wen Zhou*, etc. "A robust approach for privacy data protection: IoT security assurance using generative adversarial imitation learning." IEEE Internet of Things Journal. 2021,9(18):17089-17097.