Edge Computing in Internet of Things: Lattice-Based and Split Encryption for Post-Quantum Data Security - Man, Zhenlong; Yu, Ze; Yu, Jiahui; Gao, Chang; Meng, Xiangfu (IEEE internet of things journal, 2025)
The rapid expansion of smart devices and applications within the Internet of Things (IoT) has resulted in an unprecedented surge of data, particularly image data, generated at the network edge. This imposes significant pressure on traditional centralized cloud computing paradigms and introduces severe challenges in terms of transmission efficiency and data security. To address these issues, edge computing-assisted IoT (EC-IoT) has emerged as a promising paradigm; however, the decentralized collection, transmission, and application of image data also exacerbate privacy risks. In this work, this article proposes a secure and lightweight EC-IoT architecture specifically tailored for efficient and resilient image transmission. Node registration and tagging mechanisms are introduced to enable identity verification without additional computational overhead, while Z-order encoding, strict hierarchical encryption, and node verification collectively ensure robust protection at minimal cost. Furthermore, we design an image encryption framework that integrates a novel 4-D cross-coupled chaotic map (4DCCM) with lattice-based cryptography, achieving strong encryption performance and seamless post-quantum security compatibility. Extensive experimental evaluations conducted on representative IoT application datasets-including Internet of Medical Things (IoMT), Internet of Vehicles (IoV), and Industrial IoT (IIoT) scenarios-demonstrate the effectiveness of the proposed scheme, achieving an average information entropy of 7.9993 while maintaining low computational overhead. This work contributes a secure, efficient, and post-quantum-resilient encryption framework, particularly suited for large-scale real-time transmission of visual information within next-generation IoT environments.