Flexible and Fine-Grained Access Control for EHR in Blockchain-Assisted E-Healthcare Systems
Bibliographic record
Abstract
It is of the utmost importance to achieve flexible and fine-grained access control of electronic health records (EHR) in smart elderly healthcare (SEH) for providing high-quality healthcare services for the elderly and protecting their privacy simultaneously. In this paper, a flexible, fine-grained, and elderly-centric access control scheme is presented for EHR data in SEH. In the proposed scheme, Ciphertext Policy Attribute Based Encryption (CP-ABE), permission token, dual-key regression, and blockchain techniques are leveraged to realize multi-dimensional access control of EHR data in terms of data generation time, data user properties, access times, and access period. Moreover, a novel token segmentation algorithm is designed to transfer access rights between doctors efficiently for multi-party diagnosis and treatment. Since the elderly can define the attributes of users accessing his/her EHR data, the access number, the access time, and the access range of data from the time dimension of data generation with the cooperation of the Smart Elderly Healthcare (SEH) institution, the privacy of EHR data of the elderly is well protected. The security analysis demonstrates that our scheme can achieve EHR ciphertext indistinguishability under chosen-plaintext attacks and token unlinkability and unforgeability under data users’ collusion attacks. The experimental results show that our scheme performs well in terms of time cost and computational overhead.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".