DeepPAR and DeepDPA: Privacy Preserving and Asynchronous Deep Learning for Industrial IoT

Abstract

Industrial Internet of Things (IIoT) is significant of building powerful industrial systems and applications. Deep learning has provided a promising opportunity to extract useful knowledge by utilizing vast amounts of data in IIoT. However, lacking of massive public datasets will lead to low performance and overfitting of the learned model. Therefore, the federated deep learning over distributed datasets has been proposed. Whereas, it inevitably introduces some new security challenges, i.e., disclosing participant's data privacy. However, existing methods cannot guarantee each participant's data privacy in a learning group. In this article, we propose two privacy-preserving asynchronous deep learning schemes [privacy-preserving and asynchronous deep learning via re-encryption (DeepPAR) and dynamic privacy-preserving and asynchronous deep learning (DeepDPA)]. Compared to the state-of-the-art work, DeepPAR protects each participant's input privacy while preserving dynamic update secrecy inherently. Meanwhile, DeepDPA enables to guarantee backward secrecy of group participants in a lightweight manner. Security analysis and performance evaluations on real dataset show that our proposed schemes are secure, efficient, and effective.