Efficient Dual-Revocation CP-ABE for Secure and Fine-Grained Data Sharing in IoMT

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Abstract
The Internet of Medical Things (IoMT) enables large-scale sensing and cloud-based sharing of sensitive medical data, where access control must remain fine-grained, dynamic, and robust. In practical healthcare scenarios, access privileges frequently change due to real-world incidents such as physician resignation, role reassignment, emergency response, or patient consent withdrawal, which require both user-level and attribute-level revocation mechanisms. To address these challenges, we propose a Dual Revocable CP-ABE (DABE) scheme that unifies direct and indirect revocation within a single framework, to support diverse medical data sharing scenarios. The proposed system allows data owners to flexibly select revocation modes based on operational needs: indirect revocation efficiently handles large-scale updates for remote medical records via a semi-trusted cloud server, while direct revocation enables immediate access termination through ciphertext updates for highly sensitive data. This dual-mode design improves system robustness and fault tolerance, thus avoiding a single point of failure. Moreover, DABE supports verifiable outsourced decryption to accommodate resource-constrained IoMT devices. Formal security analysis proves IND-CPA security and resistance to collusion attacks, while experimental results demonstrate practical efficiency: the decryption time remains nearly constant (approximately 15–20 ms) regardless of policy complexity, achieving an improvement of over two orders of magnitude compared to existing schemes, with only modest overhead in other phases. These results show that DABE is well-suited for large-scale, dynamic IoMT systems.
Funding Information
  • National Natural Science Foundation of China (U25A20429)
  • National Natural Science Foundation of China (62332018)
  • National Natural Science Foundation of China (U2336204)
  • National Natural Science Foundation of China (U19A2066)
  • Natural Science Foundation of Sichuan Province (2025ZNSFSC0497)
  • Natural Science Foundation of Sichuan Province (2023YFG0112)
  • Science and technology Project of State Grid Corporation of China (5700-202355311A-1-1-ZN)
  • Outstanding Talents Training Fund in Shenzhen (5700-202355311A-1-1-ZN)
  • Outstanding Talents Training Fund in Shenzhen

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