A Novel Approach to Quantum-Resistant Selective Encryption for Agricultural Sensors with Limited Resources

Selective Encryption involves extracting critical features from the data and applying highly secure encryption to those features, while the remaining data is stored using less resource-intensive encryption techniques. Discrete Wavelet Transforms (DWT) provides a means to extract these essential features. Previous works on selective encryption using DWT have explored hardware-specific implementations, such as using a General Purpose GPU (GPGPU). However, in the context of agricultural images captured by edge devices with limited computational capabilities, leveraging a GPGPU would introduce additional hardware requirements and restrict application potential. We present a selective encryption methodology utilizing parallel CPU processing to accelerate calculations, addressing these limitations. Given the advancements in quantum computing, there is a need to ensure the employed encryption methods are secure against potential quantum attacks. We implement NIST-proposed standards: ML-KEM-1024 for key encapsulation and ML-DSA for signature verification, ensuring quantum-resistant security. Our approach provides a security analysis and performance evaluation. We demonstrate significant visual degradation of encrypted data, with mean PSNR of 4.7201 decibel (dB) and SSIM of 0.0003, indicating strong resistance to statistical attacks. Performance improvements range from 21.47% to 52.43% compared to full AES-256 encryption across various file sizes. We discuss optimizations for handling different data sizes and compare our approach's security and performance with existing state-of-the-art methods. This MLPQE method offers a balanced solution for securing agricultural images on resource-constrained edge devices while ensuring long-term data protection against emerging quantum threats.