A fuzzy vault implementation for securing revocable iris templates
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".