Flexible and Fine-Grained Access Control for EHR in Blockchain-Assisted E-Healthcare Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it