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

Moqurrab，Syed Atif1; Anjum，Adeel2; Tariq，Noshina3; Srivastava，Gautam4

2022

10.1109/TII.2022.3179536

IEEE Transactions on Industrial Informatics   影响因子和分区

1551-3203

1941-0050

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.

Computational modeling Couplings Data privacy Data Privacy Industrial Internet of Things Machine learning Mathematical models Privacy Semantic Privacy Semantics

SCI ; EI

英语

其他

NSERC Canada[RGPIN-2020-05363]

Automation & Control Systems ; Computer Science ; Engineering

Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Industrial

WOS:000880654600098

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20222412227319

5G mobile communication systems ; Couplings ; Internet of things ; Learning systems ; Privacy-preserving techniques ; Quality of service

Telecommunication; Radar, Radio and Television:716 ; Radio Systems and Equipment:716.3 ; Telephone Systems and Related Technologies; Line Communications:718 ; Data Communication, Equipment and Techniques:722.3 ; Computer Software, Data Handling and Applications:723 ; Data Processing and Image Processing:723.2

2-s2.0-85131718532

Scopus

1.Department of Computer Science, FCAI, Air University,Islamabad, and Department of Computer Sciences, COMSATS University Islamabad, Pakistan
2.Department of Information Technology, Quaid-i-Azam University Islamabad, Pakistan and Southern University of Science and Technology (SUSTECH), Shenzhen, China
3.Department of Computer Science, Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan
4.Department of Math and Computer Science, Brandon University, Brandon, MB, Canada, R7A 6A9 as well with the Research Centre for Interneural Computing, China Medical University, Taichung 40402, Taiwan

Moqurrab，Syed Atif,Anjum，Adeel,Tariq，Noshina,et al. Instant_Anonymity: A Lightweight Semantic Privacy Guarantee for 5G-enabled IIoT[J]. IEEE Transactions on Industrial Informatics,2022,PP(99):1-1.

Moqurrab，Syed Atif,Anjum，Adeel,Tariq，Noshina,&Srivastava，Gautam.(2022).Instant_Anonymity: A Lightweight Semantic Privacy Guarantee for 5G-enabled IIoT.IEEE Transactions on Industrial Informatics,PP(99),1-1.

Moqurrab，Syed Atif,et al."Instant_Anonymity: A Lightweight Semantic Privacy Guarantee for 5G-enabled IIoT".IEEE Transactions on Industrial Informatics PP.99(2022):1-1.