Addressing Privacy in a Federated Identity Management Network for EHealth
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
E-health networks can provide integrated services to patients and health care workers that are more broadly accessible by leveraging Internet technology and electronic health records. However, issues of security and privacy must be addressed. In particular, compliance with relevant privacy legislation must be established. Federated identity management can enable users and service providers to securely and systematically manage identities and user profiles in a single sign on framework that controls access to personal information. In this paper, we use a simple ePrescription scenario to analyze the business and technical issues that need to be addressed in a Liberty Alliance federated identity management framework. We look at the potential impact of privacy compliance on three existing components of the framework (Discovery Service, Identity Mapping Service, Interaction Service) as well as a fourth component (Audit Service) that has been proposed to address potential privacy breeches in Liberty Alliance.
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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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