A Quantum-Resistant Lightweight Hierarchical Privacy Protection Scheme for Traffic Images

The Internet of Things (IoT) technology, through the deployment of sensors and intelligent traffic cameras, facilitates the collection, processing, and analysis of traffic flow, vehicle density, and road conditions. However, traffic image data contains a substantial amount of personal sensitive information. Therefore, implementing hierarchical encryption on images is crucial for preventing the leakage of personal privacy and ensuring data is used in compliance with regulations. Moreover, the rise of quantum computing poses a substantial threat to existing key distribution systems, especially for resource-constrained IoT devices. To address these challenges, this article presents a lightweight hierarchical privacy protection scheme for traffic images that addresses key adaptability, quantum threats, and hierarchical privacy protection. The scheme includes a lattice-based public-key algorithm with updatable public and private keys to ensure quantum-resistant and forward security. It also introduces, for the first time, a hierarchical tree-structured encryption scheme based on the SHA3 hash function and the homomorphic property of compressed sensing, allowing high-permission users to generate keys for low-permission users without compromising the quality of image decryption. Additionally, the scheme includes a compressed sensing public-key encryption algorithm based on chaotic systems and the difficulty of matrix factorization, which supports sampling of images of any size, enhancing adaptability and reducing resource consumption. Tests indicate that this image encryption algorithm can resist various statistical analyses, achieving a high number of pixel change rate of 99.6277%. In summary, our research significantly contributes to the effective safeguarding of personal data security in resource-constrained IoT environments, particularly in the face of potential quantum computing threats.