Systematic investigation of privacy preservation techniques for industrial IoT-enabled critical edge network Infrastructure

Systematic investigation of privacy preservation techniques for industrial IoT-enabled critical edge network Infrastructure

연구 분야: Cryptography

학회: Cluster Computing

New trends in network architecture have resulted from the introduction of Internet of Things (IoT)-powered innovations. As an ecosystem of devices, sensors, applications and associated network systems, that connect to collect, monitor and analyze data for industrial operations, the Industrial Internet of Things exemplify how their deployment has led to the interconnection of heterogeneous devices and edge networks. This has raised concerns related to device heterogeneity, security and privacy of network infrastructure, which, if unaddressed, leads to the evasion of user and device privacy. Thus, enhancing user confidentiality, enabling secure communication and control of data flows within the edge networks, are essential. For user and network infrastructure security, privacy preservation strategies, such as anonymization, perturbation, location privacy, and differential privacy have been deployed. The privacy-preservation method demonstrated by Dwork’s differential privacy was found to be limited in protecting the Industrial IoT-enabled physical infrastructure. In this paper, we analyze; privacy protection requirements, the threat model with the edge network infrastructure, limitations, demerits and merits of these techniques. Finally, we investigate privacy protection techniques used within four major components of Industrial IoT enabled, critical edge network infrastructure based application which include; Networks (SDN), Data Analytics (Fog Computing), Smart Grid (Intelligent Sensor) and Applications (Pre-processing). These findings are highlighted, with suggestions of areas for future research.