Federation Chain for Data Privacy Protection in Industrial Internet of Things: The Perspective From 5G Core Networks
Xiaojie Wang, Tengfei Li, Xuanrui Xiong, Yunli Gao, Zhaolong Ning · IEEE Internet of Things Journal · 2024
The Industrial Internet of Things (IIoT) faces serious data privacy issues, such as the risk of data leakage during aggregation and transmission. However, existing studies rarely consider data privacy protection from the perspective of 5G core networks (CNs). This article proposes a federation chain-based data privacy protection system for the control plane in 5G CN to facilitate secure and decentralized communications between IIoT devices and other networks components, enhancing data integrity and confidentiality. Using XPRO instrument, 5G CN signaling storm simulation test platform, Free5GC, UERANSIM simulator, and Kali platform, the system simulates and generates realistic control plane data streams in IIoTs. To prevent unauthorized data access, we design an authentication algorithm based on the Bulletproofs zero-knowledge proof technique. Additionally, we implement a data encryption and decryption algorithm based on the Paillier partially homomorphic encryption for user privacy. We design a foundation model-based anomaly flow detection and analysis module to improve the security of the system for anomalous signaling flows. The feasibility and effectiveness of the system are validated on a 5G CN simulation testing platform, and experimental results show that the proposed approach ensures robust and scalable IIoT data privacy protection.