Scalable attribute encryption of semi-structured data in IoT based on improved recurrent neural networks

Attribute encryption technology presents increasing challenges to the scalability of IoT systems as the number of devices and data volume grow. This study designs a scalable attribute encryption method for IoT semi-structured data using an enhanced recurrent neural network (RNN). The ant colony algorithm optimizes queries on semi-structured data, generating efficient query plans. A backpropagation (BP) neural network learns similarity feature vectors, eliminates redundant data through batch processing, and applies optimal classification matrices to reduce feature dimensions. Additionally, a recursive neural network with multiple hidden layers extracts hierarchical abstract features, capturing complex patterns from semi-structured data. For encryption, a quantum cellular neural network’s chaotic generator dynamically produces keys, while XOR operations and balance coefficients enable adaptive encryption and storage. Experimental results demonstrate that the proposed method achieves an encryption strength of approximately 0.98 (normalized to 1.0), operates within 150 ms, and maintains low memory consumption. Moreover, the ciphertext ASCII codes exhibit a uniform distribution, providing resistance against frequency-based plaintext and ciphertext attacks. By integrating machine learning, optimization algorithms, and quantum-inspired encryption, this framework addresses scalability and security requirements in large-scale IoT deployments, ensuring efficient data processing and robust protection against evolving threats.