Securing Healthcare Records in the Cloud Using Attribute-Based Encryption
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
<p>Cloud Computing has attracted interest as an efficient system for storing and access of data. Sharing of personal electronic health record is an arising concept of exchanging health information for research and other purposes. Cconfidentiality except for authorized users, and access auditability are strong security requirements for health record. This study will examine these requirements and propose a framework for healthcare cloud providers that will assist in securely storing and sharing of patient’ data they host. It should also allow only legitimate users to access portion of the records' data they are permitted to. The focus will be on these precise security issues of cloud computing healthcare and how attribute-based encryption can assist in addressing healthcare regulatory requirements. The proposed attribute-based encryption guarantees authentication, data confidentiality, availability, and integrity in a multi-level hierarchical order. This will allow the healthcare provider to easily add/delete any access rule in any order, which is considered beneficial particularly in medical research field.</p>
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.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.010 |
| 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