Secure and Privacy preserving Biometric based User Authentication with Data Access Control System in the Healthcare Environment
Bibliographic record
Abstract
In recent years, there has been a tremendous growth worldwide in healthcare information systems to provide personalized services smartly. A digital health documentary EHR (Electronic health record) is utilized to keep users sensitive medical or personal data records, which allows medical professionals to access a patient's information in an insecure environment. Thus, providing security and privacy to e-health information is of utmost importance as private sensitive or safety critical data of the users is transmitted over a wireless channel. Motivated by this fact, in this work, we have developed a biometric based lightweight user authentication system that provides users personalized services securely, safely and efficiently. In the proposed authentication, a lightweight data access control process has been described so that only legal users can access the data as per their capability. Further, to maintain user privacy, instead of a user's global identifier, his temporary local identifier is used for communication whereas the system is designed in such a way that in case of emergency, if required, the user global identifier can be recovered. Finally, formal and informal security verification results and performance evaluation comparison demonstrates that the proposed authentication scheme is secure enough to be used in a healthcare environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".