Internet of Things (IoT) is predominantly applied in industrial environments, mainly under the umbrella term Industry 4.0. This revolution provides the opportunity to revolutionize production and manufacturing via utilization of interconnected sensor devices and innovative computing technologies such as Machine Learning (ML), Reinforcement Learning (RL), and Federated Learning (FL). Subsequently, IoT security stands as a leading barrier in delaying widespread IIoT adoption, triggering an increase in research publications. Authors acknowledge the need to survey existing academic literature to shift the paradigm, highlighting security and privacy requirements and their prevalence. This paper exhibits contributions which include primarily, a bibliometric evaluation of IIoT security and privacy preserving methods. It enables the first goal-driven bibliometric mapping of research focusing explicitly on privacy-preserving mechanisms in IIoT. It unifies both performance analysis (quantitative publication and citation metrics) and science mapping (network visualization of research themes and collaborations). This technique includes statistically analyzing available articles within a specified period and location, establishing links among these articles. For this evaluation, data underwent in-depth preprocessing and analysis using dedicated tools such as VOS viewer and the Biblioshiny library, dedicated for mapping and analyzing information. The assessment includes publications from 2014 to 2025 in the assessment process. An overall count of 137 Computer Science publications is considered subsequently screening an initial dataset of 940 Scopus-indexed records. The first objective is collection of most influential articles. Second objective is to assessing the contributions of documents based on countries and researchers. Third objective is intended to identify the most persuasive sources, keywords, and emerging topics via bibliographic coupling. Additionally, the analysis delved into comparative analysis of security and privacy preserving methods across IIoT networks, discussing their distinct features, advantages, and drawbacks. These methods are examined with their unique features, leverages, and detriments. It reveals hidden knowledge structures and emerging research fronts that standard reviews or survey have not adequately addressed. Co-authorship networks indicate strong international collaboration, particularly among research institutions and industry partners. Keyword and citation analyses show that blockchain-based security, secure data sharing, and lightweight encryption are the dominant research themes, while emerging topics include AI-driven intrusion detection and privacy-aware edge computing. Considering on bibliometric insights and comparative analysis, the study suggested ADP based IDS for IIoT a framework that links technological foundations, research clusters, and application domains. This framework acts as a roadmap for researchers and policymakers to develop integrated approaches that optimize the trade-offs between data utility, security, and privacy compliance in industrial environments. The study expands traditional bibliometric boundaries by including meta-analysis of policy-driven research output, emphasizing how international data protection regulations affects academic trends and cross-country collaborations in privacy-preserving IIoT. It acts as the first bibliometric study specifically focused on the intersection of privacy-preserving mechanisms and data-driven IIoT, providing actionable insights for researchers and practitioners.
Kumar et al. (Fri,) studied this question.