Bibliographic record
Abstract
The Fuzzy Vault scheme was proposed as a means to improve the security of biometric technologies. However, the originally proposed Reed-Solomon (RS) decoding algorithm was not well understood and the CRC decoder, introduced as an alternative, remains the most popular in literature today. Despite its simplicity, CRC decoders contain significant flaws which motivate us to investigate the potential of RS decoders. In this paper, we discuss the role of RS codes in Fuzzy Vault and details a Berlekamp-Massey decoder as originally envisioned. However, due to the significant amount of erasures in a RS code word in Fuzzy Vault leading to poor decoder performance, we propose instead an alternate decoder based on the Euclidean algorithm, which considers the original approach to RS codes. Our results show that the RS decoder can achieve decoding speed significantly greater than any CRC decoders while maintaining information-theoretic security, which are independent of advances in computing power. We also propose an improvement to the CRC decoder as a means to evaluate the security of a Fuzzy Vault and found that many existing implementations using CRC decoding are vulnerable to brute-force attacks.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".