A holistic framework for strengthening security of healthcare data through encryption utilizing blockchain technology

Healthcare data security is increasingly critical due to the sensitive nature of patient information and the rising prevalence of cyber-attacks on medical systems. Existing techniques to security enhancement are evaluated, demonstrating their limits and emphasizing the urgent need for novel solutions. To address these issues, this study proposes a Blockchain-integrated Advanced Encryption Standard (BCT-AES) framework to enhance the privacy, integrity, and security of healthcare data. The framework combines Convolutional Neural Networks (CNN) for feature extraction, Decision Tree (DT) and Logistic Regression (LR) for classification, and AES encryption integrated with blockchain technology to provide a decentralized, tamper-proof storage solution. In this hybrid design, CNN extracts meaningful patterns from patient records and medical images, which are then classified by DT and LR models to facilitate predictive analytics while maintaining data confidentiality. Sensitive information is encrypted using AES before being recorded on the blockchain, ensuring robust access control and immutability. It further strengthens the data security and integrity when applied with AES. The results are carried out with Python software, meaning the proposed method has practicality in real life. The performance of the proposed BCT-AES framework was quantitatively validated, showing an average encryption time of 1.12 milliseconds, significantly outperforming existing methods such as AES-CP-IDABE (10.52 ms), Enhanced AES (20.9 ms), and AES-CBC (2.4 ms). Additionally, the framework demonstrated high classification accuracies, with DT achieving 99% and LR achieving 89%, indicating reliable predictive capabilities. By integrating deep learning with advanced encryption and blockchain, the proposed approach offers a practical and efficient solution for secure healthcare data management, supporting real-time analytics while preserving patient privacy.