Enhancing E-Healthcare Security with Hash Lampel Hessenberg Wavelet Transform and Federated Cryptographic Neural Networks

In the healthcare industry, data sharing provides a comprehensive view of a patient's health history, enhancing treatment, research, and diagnosis. However, securing medical data remains a critical challenge in e-health applications, as traditional security methods often struggle against malicious and unauthorized attacks. Among the various techniques employed to safeguard medical images, watermarking has emerged as a promising approach. This research introduces a pioneering technique, Hash Lampel Hessenberg Wavelet Transform-Federated Reinforcement Cryptographic Fuzzy Neural Network-Based User Authentication Algorithm (HLHWT-FRCFNN-UAA), to improve security in e-health applications. The approach integrates advanced methodologies such as SHA-256 encryption, Lempel-Ziv-Welch compression, Hessenberg decomposition, and Discrete Wavelet Transform to ensure robust watermarking of medical images. To further strengthen security, a federated reinforcement cryptographic fuzzy neural network-based user authentication algorithm is implemented, leveraging homomorphic encryption, Shamir secret sharing with Q-learning, and fuzzy neural networks to authenticate data before secure cloud storage. Compared to existing methods, this approach achieved superior results such as 98.6% of detection accuracy, 98.1% of specificity, 98.41% of sensitivity, 98.4% of F1-score, and 98% of normalized cross-correlation. Experimental analysis validates its effectiveness across various e-healthcare security and image watermarking datasets, demonstrating a significant advancement in medical data security.