Instant_Anonymity: A Lightweight Semantic Privacy Guarantee for 5G-enabled IIoT

Data publication and sharing are critical components of assessing network infrastructures in the Internet of Things (IoT) for Quality of Service (QoS) enhancement. Especially, the advancement in communication technology (e.g., 5G/6G) enables the improvement of the current bottlenecks in Industrial IoT (IIoT). Recent approaches remove raw data and its source to achieve a privacy guarantee. However, the data is already anonymized; it still reveals the victim's extra information using linkage attacks. When data is updated, combined, or noise is introduced as part of conventional privacy protection approaches such as k-anonymity, l-diversity, or differential privacy, the usefulness of the released data is diminished, however, posing data utility and computation constraints. In recent years, lightweight privacy-preservation techniques have been proposed for these reasons. However, most focus on syntactic privacy instead of semantic privacy guarantee. Therefore, this paper proposes a lightweight semantic privacy-preservation framework for maintaining privacy with high utility efficiency. The proposed paradigm ensures semantic privacy by combining probabilistic random sampling with Instant_Anonymity. Compared to k-anonymity, the suggested model demonstrates improved data utility with lower utility errors of 0.00036 and 0.41 for KL-Divergence and Query-error, respectively. The classification accuracy is improved by 0.2%. Additionally, in computation time, the proposed approach is simpler to implement than existing state-of-the-art lightweight privacy-preserving strategies.