Access control and data privacy are two of the main necessities in managing electronic health records (EHRs) across distributed domain. There are privacy gaps that expose EHRs to risks like unauthorized access by unaffiliated medical personnel. Traditional attribute-based encryption (ABE) allows encryption based on user attributes but is unable to incorporate data-specific attributes, such as the type of medical information included in the record or the potential physician. This paper introduces a novel approach that integrates ABE with large language models (LLMs) and blockchain technology to enhance security and contextual access control in EHR systems. Specifically, a domain-specific LLM, such as ClinicalBERT, is leveraged to automatically extract semantic data attributes from unstructured medical records, enabling a more granular and context-aware encryption process. By embedding both user and data attributes into the ABE framework, access policies are dynamically refined, ensuring that only authorized users can view specific types of medical information. Furthermore, blockchain’s immutable ledger enhances trust, streamlines attribute revocation, and fortifies the system against unauthorized modifications and security threats. The proposed framework significantly strengthens EHR privacy by integrating machine learning-driven attribute extraction with cryptographic access control, outperforming existing schemes in both security and flexibility. Evaluations validate the effectiveness of the proposed framework in preventing unauthorized access while maintaining efficient and transparent data management.
Nekouie et al. (Tue,) studied this question.