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

Abstract

Data publication and sharing are critical components of assessing network infrastructures in the Internet of Things for quality-of-service enhancement. Especially, the advancement in communication technology (e.g., 5G/6G) enables the improvement of the current bottlenecks in the Industrial Internet of Things. Recent approaches remove raw data and their source to achieve a privacy guarantee. However, the data are already anonymized; these still reveal the victim’s extra information using linkage attacks. When data are updated and combined or noise is introduced as part of conventional privacy protection approaches, such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -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 of the focus is on syntactic privacy instead of semantic privacy guarantee. Therefore, this article 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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity, the suggested model demonstrates improved data utility with lower utility errors of 0.00036 and 0.41 for Kullback–Leibler divergence and query error, respectively. The classification accuracy is improved by 0.2%. In addition, the proposed approach is simpler to implement in computation time than the existing state-of-the-art lightweight privacy-preserving strategies.