Microaggregation-Based Defense Against Synonymous Linkages in IIoT Asset Tracking

In the era of Industry 4.0, the proliferation of location-based services (LBS) 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 paper 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.