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Secure electronic health record access control via blockchain, dual-attribute encryption, and large language

Atefeh Nekouie1, Majid Vafaei Jahan2, Mohammad Hossein Moattar3

  • 1Department of Computer Engineering, Ma.C., Islamic Azad University, Mashhad, Iran.

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This study enhances electronic health record (EHR) security by integrating attribute-based encryption (ABE) with large language models (LLMs) and blockchain. This approach enables context-aware access control, preventing unauthorized access to sensitive medical data.

Keywords:
Attribute-based encryption (ABE)BlockchainElectronic health records (EHR)Large language models (LLMs)PrivacySecurity

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Area of Science:

  • Computer Science
  • Information Security
  • Medical Informatics

Background:

  • Managing electronic health records (EHRs) requires robust access control and data privacy.
  • Existing attribute-based encryption (ABE) methods lack the ability to integrate data-specific attributes, creating privacy gaps.
  • Unauthorized access to EHRs poses significant risks to patient confidentiality.

Purpose of the Study:

  • To introduce a novel framework for enhancing EHR security and contextual access control.
  • To address the limitations of traditional ABE by incorporating data-specific attributes.
  • To improve the privacy and flexibility of EHR management systems.

Main Methods:

  • Integration of attribute-based encryption (ABE) with large language models (LLMs) and blockchain technology.
  • Utilization of a domain-specific LLM (ClinicalBERT) for semantic data attribute extraction from unstructured medical records.
  • Embedding both user and data attributes into the ABE framework for dynamic access policy refinement.
  • Leveraging blockchain's immutable ledger for enhanced trust and streamlined attribute revocation.

Main Results:

  • The proposed framework enables granular, context-aware encryption and access control for EHRs.
  • It dynamically refines access policies based on both user and data attributes.
  • The system demonstrates superior security and flexibility compared to existing schemes.
  • Evaluations confirm the framework's effectiveness in preventing unauthorized access and ensuring efficient data management.

Conclusions:

  • The novel framework significantly strengthens EHR privacy through the integration of machine learning-driven attribute extraction and cryptographic access control.
  • This approach offers a more secure and flexible solution for managing sensitive medical information.
  • The combination of LLMs, ABE, and blockchain technology provides a robust defense against unauthorized access and data breaches.