Privacy-Preserved Data Sharing Towards Multiple Parties in Industrial IoTs
By Xu Zheng, Zhipeng Cai. IEEE Journal on Selected Areas in Communications, 2020
367 citations recorded by the source corpus.
The effective physical data sharing has been facilitating the functionality of Industrial IoTs, which is believed to be one primary basis for Industry 4.0. These physical data, while providing pivotal information for multiple components of a production system, also bring in severe privacy issues for both workers and manufacturers, thus aggravating the challenges for data sharing. Current designs tend to simplify the behaviors of participants for better theoretical analysis, and they cannot properly handle the challenges in IIoTs where the behaviors are more complicated and correlated. Therefore, this paper proposes a privacy-preserved data sharing framework for IIoTs, where multiple competing data consumers exist in different stages of the system. The framework allows data contributors to share their contents upon requests. The uploaded contents will be perturbed to preserve the sensitive status of contributors. The differential privacy is adopted in the perturbation to guarantee the privacy preservation. Then the data collector will process and relay contents with subsequent data consumers. This data collector will gain both its own data utility and extra profits in data relay. Two algorithms are proposed for data sharing in different scenarios, based on whether the service provider will further process the contents to retain its exclusive utility. This work also provides for both algorithms a comprehensive consideration on privacy, data utility, bandwidth efficiency, payment, and rationality for data sharing. Finally, the evaluation on real-world datasets demonstrates the effectiveness of proposed methods, together with clues for data sharing towards Industry 4.0.
Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems
View Paper: https://www.semanticscholar.org/paper/1ee96c89a357b2892f2f08b5e938e401c2d5a357
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Citations in the corpus
- Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT (2021, background): “especially in need of security monitoring [6], [7], [4].”