Securing Telehealth Applications in a Web-Based e-Health Portal
Bibliographic record
Abstract
Telehealth applications can deliver medical services to patients at remote locations using telecommunications technologies, such as the Internet. At the same time, such applications also pose unique security challenges. First, the trust issue becomes more severe due to the lack of visual proofs in telehealth applications. The public key infrastructure (PKI) is insufficient for providing the same kind of trust a patient may attain during a face-to-face service. Second, telehealth services, such as tele-monitoring or tele-consultant, naturally demand a systematic organization of users, roles, resources, and flows of information. Existing access control mechanisms in an e-health system are usually incapable of dealing with such workflow-based services. This paper provides cost-efficient solutions to those issues in the context of a Web-based e-health portal system. First, we propose a PKI-like infrastructure for establishing trust between users using biometrics-based authentication and hierarchies of trust. Second, we develop an access control method for workflow-based telehealth services using a rule-based module already available in the portal 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 | 0.006 | 0.009 |
| 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 | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".