Deep learning-based data privacy protection in software-defined industrial networking

Abstract

The industrial Internet connects equipment to the network and utilizes the data generated to assist businesses. Industrial big data is the result of data accumulation; thus, the industrial Internet has to adopt new technologies—namely, software-defined industrial networks (SDIN) —to keep up with these developments. This study suggests a deep differential privacy data protection algorithm based on SDIN. The deep learning model is analyzed and integrated with differential privacy to provide the process framework for the deep differential privacy data protection algorithm. The equivalent model of the generative adversarial network is used to allow the attacker to obtain the optimal fake samples. The balance between dataset availability and privacy protection is achieved by implementing parameter tuning on the deep differential privacy model. The experimental results show that the proposed algorithm has strong industrial data privacy protection and high data availability and can effectively guarantee the privacy security of industrial data.