A secure revocable personal health record system with policy-based fine-grained access control
Bibliographic record
Abstract
Collaborative sharing of information is becoming much more needed technique to achieve complex goals in today's fast-paced tech-dominant world. In our context, Personal Health Record (PHR) system has become a popular research area for sharing patients information very quickly among health professionals. PHR systems store and process sensitive information, which should have proper security mechanisms to protect data. Thus, access control mechanisms of the PHR should be well-defined. Secondly, PHRs should be stored in encrypted form. Therefore, cryptographic schemes offering a more suitable solution for enforcing access policies based on user attributes are needed. Attribute-based encryption can resolve these problems. We have proposed a framework with fine-grained access control mechanism that protects PHRs against service providers, and malicious users. We have used the Ciphertext Policy Attribute Based Encryption system as an efficient cryptographic technique, enhancing security and privacy of the system, as well as enabling access revocation in a hierarchical scheme. The Web Services and APIs for the proposed framework have been developed and implemented, along with an Android mobile application for the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".