We have witnessed significant technological advancement over the past few years, including the Internet of Things (IoT). The IoT’s ability to connect consumer appliances to the internet has changed the way we live. As a result of the significant benefits that IoT has brought to household usage, it has become a topic of discussion for research departments, leading to its expansion into industrial sectors, commonly known as the Industrial Internet of Things (IIoT). IIoT enables automation and the use of intelligent machines to improve product manufacturing processes and enhance our lives as customers. However, as IIoT-enabling technology and applications continue to grow, security issues and privacy protection challenges become harder to manage, which frequently results in data breaches and sensitive information disclosures. This paper first explains what the reader needs to know about the IIoT system architecture in Industry 4.0 to make it easier for them to understand. Second, a hacking scenario is utilized as a methodology to conduct an in-depth analysis of various security issues, as well as their impacts and countermeasures, for each level of the IIoT architecture. Additionally, our hacking scenarios present a variety of targets from which malicious actors can launch their assaults. Third, we provide a thorough review of the various blockchain solutions currently being employed to protect IIoT systems. Finally, this paper draws to a close by outlining certain gaps and potential solutions that could be investigated in subsequent studies to strengthen security and enhance privacy for IIoT systems.

The rapid development of the mobile Internet coupled with the widespread use of intelligent terminals have intensified the digitization of personal information and accelerated the evolution of the era of big data. The sharing and publishing of various big data brings convenience and also increases the risk of personal privacy leakage. In order to reduce users’ privacy leakage that may be caused by data release, many privacy preserving data publishing methods have been proposed by scientists in both academia and industry in the recent years. However, non-numerical sensitive information has natural semantic relevance, and therefore, synonymous linkages may still exist and cause serious privacy disclosures in privacy protection methods based on an anonymous model. To address this issue, this paper proposes a privacy preserving dynamic data publishing method based on microaggregation. A series of indicators are accordingly designed to evaluate the synonymous linkages between the non-numerical sensitive values which in turn facilitate in improving the clustering effect of the microaggregation anonymous method. The dynamic update program is introduced into the proposed microaggregation method to realize the dynamic release and update of data. Experimental analysis suggests that the proposed method provides better privacy protection effect and availability of published data in contrast to the state-of-the-art methods.

In the era of Industry 4.0, the proliferation of location-based services (LBSs) and Industrial Internet of Things (IIoT) asset tracking has revolutionized supply chains and smart manufacturing, offering unprecedented benefits but raising privacy concerns. Continuous asset monitoring and geolocation data can potentially expose confidential information. Existing privacy methods like k-anonymity and l-diversity can preserve the location privacy of IIoT asset tracking devices. However, privacy concerns persist due to inadequate protection against synonymous linkages and probability-distributed attacks. The inherent semantic relevance of sensitive location data may maintain synonymous linkages, and the uneven distribution of historical query probabilities in anonymous spatial regions amplifies the likelihood of adversaries identifying the precise location of IIoT asset tracking devices, resulting in significant privacy breaches. To address these challenges, this article introduces the location diversity-based microaggregation (LDM) method, which enhances privacy protection for IIoT asset tracking by mitigating synonymous linkages and probability-distributed attacks. LDM employs indicators to evaluate synonymous linkages, improving clustering in anonymous microaggregation. It also introduces a caching mechanism to reduce computational costs and prevent unnecessary connections to untrusted LBS providers, reinforcing privacy preservation. Experimental analysis validates the efficiency of the proposed algorithm, demonstrating a reduction in synonymous linkages and superior privacy protection compared to existing state-of-the-art methods. This research represents a crucial step in ensuring robust privacy within IIoT environments and addressing the evolving landscape of LBS in Industry 4.0.

As widely adopted privacy models in privacy-preserving data publishing (PPDP), k-anonymity and ℓ-diversity have been extensively studied by researchers to enable the release of useful information while preserving data privacy. However, existing methods suffer from several limitations. They often rely on single-dimensional privacy models and lack unified metrics for accurately quantifying privacy leakages. Many approaches overlook the impact of semantic similarity and adversarial prior and posterior beliefs among sensitive attributes and frequently employ suboptimal similarity measures that fail to account for the heterogeneous nature of quasi-identifiers, thereby degrading both privacy protection and data utility. To address these challenges, this paper proposes CAMDP, a unified clustering-based anonymization method for privacy-preserving data publishing with multidimensional privacy quantification. CAMDP constructs equivalence classes that satisfy k-anonymity while simultaneously enhancing sensitive attribute diversity, reducing semantic similarity, and limiting divergence between prior and posterior adversarial beliefs. A unified multidimensional metric is introduced to jointly quantify privacy leakage and information loss, guiding the anonymization process. Additionally, a similarity-aware distance metric tailored to mixed-type quasi-identifiers is employed to reduce information loss. Experimental results on three benchmark datasets, Adult, Careplans, and Airline, demonstrate that CAMDP consistently outperforms state-of-the-art methods. Across all tested configurations, CAMDP achieves the lowest average privacy leakage (0.1235, 0.0795, and 0.1855, respectively), lower average information loss (0.626, 0.636, and 0.60, respectively), and the lowest average intra-cluster dissimilarity (0.586, 0.635, and 0.573, respectively), while maintaining competitive execution time across the three datasets.