A Privacy Enhanced Facial Recognition Access Control System Using Biometric Encryption
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
With a modest adoption of biometrics for security controls, privacy remains a great concern for many individuals as biometric features, once compromised, cannot be renewed and will render protected resources vulnerable to a number of attacks by a threat agent. Several biometric encryption mechanisms have been proposed to preserve privacy, however there has been very little industry usage and implementation. In this paper, a practical biometric encryption technique is presented. The proposed approach is used to provide the desired level of privacy for stored biometric templates through anonymization. This scheme also addresses the limitation of renewability as biometric templates are fused with a biometric key, which may be renewed in the event of compromise of the biometric key. A prototype of the proposed scheme indicates that it could be a viable replacement for traditional biometric security controls with an increased confidence in the preservation of the end-user's privacy.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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