An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)

Abstract

The rapid advancements in technology have given rise to groundbreaking solutions and practical applications in the field of the Industrial Internet of Things (IIoT). These advancements have had a profound impact on the structures of numerous industrial organizations. The IIoT, a seamless integration of the physical and digital realms with minimal human intervention, has ushered in radical changes in the economy and modern business practices. At the heart of the IIoT lies its ability to gather and analyze vast volumes of data, which is then harnessed by artificial intelligence systems to perform intelligent tasks such as optimizing networked units’ performance, identifying and correcting errors, and implementing proactive maintenance measures. However, implementing IIoT systems is fraught with difficulties, notably in terms of security and privacy. IIoT implementations are susceptible to sophisticated security attacks at various levels of networking and communication architecture. The complex and often heterogeneous nature of these systems makes it difficult to ensure availability, confidentiality, and integrity, raising concerns about mistrust in network operations, privacy breaches, and potential loss of critical, personal, and sensitive information of the network's end-users. To address these issues, this study aims to investigate the privacy requirements of an IIoT ecosystem as outlined by industry standards. It provides a comprehensive overview of the IIoT, its advantages, disadvantages, challenges, and the imperative need for industrial privacy. The research methodology encompasses a thorough literature review to gather existing knowledge and insights on the subject. Additionally, it explores how the IIoT is transforming the manufacturing industry and enhancing industrial processes, incorporating case studies and real-world examples to illustrate its practical applications and impact. Also, the research endeavors to offer actionable recommendations on implementing privacy-enhancing measures and establishing a secure IIoT ecosystem.

A Systematic Review on Privacy-Aware IoT Personal Data Stores

“…Year Systematic Domain Main Goal Ogonji et al [15] ✓ IoT Privacy and security threats, attack surface, vulnerabilities, and countermeasures in IoT. Sarwar et al [25] x IoT and Fog Privacy preservation in IoT and fog-enabled IoT Zainuddin et al [16] x IoT Applications Privacy threats in IoT applications Islam et al [17] ✓ IoT and Big Data Types of privacy attacks and solutions in the IoT and Big Data contexts Zarandi et al [20] x Smart Farming Big Data privacy in smart farming Hireche et al [21] x IoMT Privacy and security in IoMT Shahid et al [23] x IoHT Data breaches and legal regulations in IoHT Alabdulatif et al [24] x IoNT Overview of IoNT and its security and privacy issues Torre et al [18] ✓ IoT Devices Privacy-preservation techniques for IoT devices Tokas et al [26] x IoT Privacy-enhancing technologies and GDPR-specific privacy principles Demertzi et al [27] x IIoT Privacy requirements in the Industrial IoT Fallatah et al [28] x PDS PDS and its advantages and disadvantages Zubaydi et al [29] ✓ IoT and Blockchain Blockchain solutions for IoT security and privacy issues Asqah and Moulahi [30] x…”

“…Demertzi et al [27] reviewed the literature on the privacy requirements of the Industrial Internet of Things (IIoT). They provided an overview of this area, including its advantages, disadvantages, challenges, and primarily privacy issues.…”

Improved Intrusion Detection Based on Hybrid Deep Learning Models and Federated Learning

“…Through the sigmoid function, the output data x ′ are compressed within the (0, 1) interval, resulting in reconstructed data x ′ with the same dimensions as the input x. The detailed process is described in Equation (5).…”

“…By embedding intelligent sensors and controllers in industrial equipment and systems, the IIoT enables real-time data collection, processing, and analysis, thereby optimizing operational efficiency, reducing costs, and increasing productivity. However, with the rapid development and widespread application of the IIoT, its security threats are also increasing, especially with frequent network intrusion events, severely threatening the security operation of industrial systems and data integrity [ 4 , 5 ].…”

Reinforcement Learning-Based Optimization for Sustainable and Lean Production within the Context of Industry 4.0

“…These devices communicate with the system, gathering significant data [8,9]. With the aid of machine learning, they analyze and utilize the accumulated data to schedule operations, predict failures, manage product inventory, and conduct quality inspections [10,11]. As a result, corresponding predictive models are developed and assume an integral role in the decision-making process conducted within smart production system [12,13].…”