A fuzzy vault implementation for securing revocable iris templates
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 recent years biometric cryptosystem evolved as a means for solving key management issues as well as protecting biometric templates. The fuzzy vault is a well known cryptotographic construction well suited for biometric systems. It has been studied theoretically as well as practically implemented in biometric systems using different biometric traits. When implemented in iris-based biometric system, the fuzzy vault faces two main challenges: (1) it requires an unordered set for successful implementation, and (2) it needs to deal with intra-class variations. In this paper, we implement a fuzzy vault based on the iris templates. A modified fuzzy vault resolves the issue of unordered set and error correction coding is used to deal with intra-class variations. An iris shuffling algorithm is also integrated into the system to ensure revocability. The proposed structure can be integrated with existing databases using binary iris templates and hence does not require the redesign of the biometric authentication system. Revocability ensures that even if the system is compromised new templates can be issued without compromising privacy of the individuals. The system is evaluated using the CASIA database and results show that the system is successful in ensuring security and revocability of the iris templates without compromising the performance.
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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 | 0.000 | 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 | 0.000 | 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