Privacy-Preserving and Secure Data Management in Medical Databases

School of Computing and Data Science, Xiamen University Malaysia, Sepang, Malaysia

mec2109506@xmu.edu.my

Abstract

Due to the exponential growth of clinical data and its inherent privacy sensitivity, ensuring robust privacy and security in medical database systems remains a critical challenge. This article reviews recent advances in Access Control, Data Encryption, and Blockchain technologies. Access control covers the framework design based on multi-institution attribute encryption, Elliptic Curve Cryptography (ECC) and Selective Ring Access Control (SRAC) mechanism. Data encryption discusses the encryption schemes of Fully Homomorphic Encryption (FHE), SFO-optimized Multi-Key Homomorphic Encryption (MHE-SFO) and Parallel AES (P-AES) combined with RSA. In blockchain applications, it introduces the permissioned public chain architecture based on fog computing and the management methodology of introducing sufficiency scoring (sfmd). Regarding these technologies, the article also points out the limitations of deep learning models, data encryption performance and searchability, and blockchain, suggesting future research to enhance data and search efficiency, privacy protection, trustworthy Artificial Intelligence (AI) and blockchain efficiency through techniques like semi-supervised learning, explainable AI, lightweight encryption and decentralized software. This article provides a comprehensive and in-depth review that can help readers in this field better understand the current and future directions.