Privacy-Preserving and Secure Industrial Big Data Analytics: A Survey and the Research Framework

The development of the Industrial Internet will generate a large amount of valuable data, known as industrial big data (IBD). By mining and utilizing IBD, enterprises can improve production efficiency, reduce costs and risks, optimize management processes, and innovate services and business models. However, industrial big data comes from various institutions in all walks of life and has features such as multi-source, heterogeneity, and multi-modality. And data sharing and trading (DS&T) occur in the Industrial Internet environment without mutual trust. These characteristics pose new challenges to analytics methods and privacy and security protection technologies. Therefore, this paper aims to provide references for privacy-preserving and secure industrial big data analytics (IBDA) from three perspectives: research framework, platform architecture, and key technologies. Firstly, we review the current state of research on theories and technologies related to IBDA. Then, we reveal three challenges to secure and efficient IBDA. We take the analytics and utilization of IBD as systematic engineering, propose the research framework for privacy-preserving and secure IBDA, and point out the specific content to be studied. Further, we design the architecture of the IBDA platform with the idea of layering, including a function model, security architecture, and system architecture. Finally, detailed research proposals and potential technologies for IBD analytics and utilization are presented from three aspects: data fusion and analytics, data privacy and security protection, and blockchain.