Privacy Protection Based on Hopfield Cross Neural Network in WBANs for Medical Images

The popularity of wearable medical data collection and surveillance devices provides real-time guarantees for the whole process of a patient's medical treatment, especially medical image data plays a key role. However, existing medical images face data leakage, pollution and vulnerability to attacks during transmission over wireless body area networks(WBANs). To address these issues, a privacy protection algorithm based on Hopfield cross neural network (HCNN) for medical data is proposed. Specifically, the HCNN model is first constructed and its dynamic behavior is analyzed, which is suitable for application to image encryption. Then, a confusion method of NZ fractal curve sorting matrix (NZ-FCSM) is designed to achieve good encryption effect. Subsequently, the secret image sharing (SIS) technique based on sharing matrix is introduced to enhance the algorithm robustness. Finally, an alignment embedding of double diamond prediction (AEDDP) method is proposed to implement lossless hiding of private information. The present issues in medical image protection include ensuring the security and effectiveness of encryption algorithms while maintaining the robustness and concealment of ciphertext data, and balancing the need for preservation with the limited resources of complex work environment. Experimental results show that the proposed algorithm achieves PSNR of 53 dB for the cipher image, more than 36 dB for the reconstructed image, and the information entropy of the secret image is over 7.99, and displays good robustness. These findings highlight the validity of the algorithm in medical image data privacy preserving applications that ensure confidentiality and extend to practical applications of concealed transmission of confidential information and secure multi-party transactions.