Cancellable multi-modal biometrie authentication for cloud based mobilityfirst like environment
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
In this research, we propose a secure multi-modal biometric authentication that will have cancellable property. Cloud servers need to have the confidential information mobile and ready in case of cancellation so that the same user can authenticate or identify using the same authentication process when joining to other clouds. However, a cloud server needs to be careful transferring biometric data to other cloud during busy time, since biometric data is sensitive to bit error rate that may adversely affect the false acceptance or rejection rate. As such another key contribution of this paper is to provide a decision making model for biometric/confidential data transfer among clouds in busy time in order to avoid network congestion. The work presented in this research is also a good fit during migration of today's internet into MobilityFirst architecture while biometric information in a locality is being shared by cloud servers and service is being provided at the same 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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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