Authentication and Access Control in e-Health Systems in the Cloud
Bibliographic record
Abstract
The opportunity to access on-demand, unbounded computation and storage resources has increasingly motivated users to move their health records from local data centers to the cloud environment. This change can reduce the costs associated with the management of data sharing, communication overhead and improve Quality of Service (QoS). Processing, storing, hosting and archiving data related to e-Health systems without physical access and control can exacerbate authentication and access control issues in this new environment. Therefore, convincing users to move sensitive medical records to the cloud environment requires implementing secure and strong authentication and access control methods to protect the data. This paper proposes a new information access method that preserves both authentication and access control in cloud-based e-Health systems. Our method is based on a zero-knowledge protocol combined with two-stage keyed access control. In each access request, based on the maximum rights of user, the minimum access is extracted. To establish secure connections between different entities in the system, a two-step combination of public key encryption and DUKPT is used. We analyze our scheme with respect to data confidentiality and resistance to common attacks on the network. Experimental results show that the proposed method tolerates a high number of concurrent authentication requests with a reasonable response time.
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 | 0.004 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".