Behavior-based access control for distributed 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
Sensitivity of clinical data and strict rules regarding data sharing have caused privacy and security to be critical requirements for using patient profiles in distributed healthcare systems. The amalgamation of new information technology with traditional healthcare workflows for sharing patient profiles has made the whole system vulnerable to privacy and security breaches. Standardization organizations are developing specifications to satisfy the required privacy and security requirements. In this paper we present a novel access control model compliant with healthcare standards based on a framework designed for data and service interoperability in the healthcare domain. The proposed model for customizable access control captures the dynamic behavior of the user and determines access rights accordingly. The model is generic and flexible in the sense that an access control engine dynamically receives security effective parameters from the subject user, and identifies the privilege level in accessing data using different specialized components within the engine. Standard data representation formats and ontologies are used to make the model compatible with different healthcare systems. The access control engine employs an approach to follow the user's behavior and navigates among engine components to provide the user's privilege to access a resource. A simulation environment is implemented to evaluate and test the proposed model.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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