Weakness of F 3 6*1429 and F 2 4*3041 for Discrete Logarithm Cryptography.
Bibliographic record
Abstract
In the past two years, there have been several dramatic improvements in algorithms for computing discrete logarithms in small-characteristic finite fields. In this paper, we examine the effectiveness of these new algorithms for computing discrete logarithms in F 3 6 ? 1429 and F 2 4 ? 3041 . The intractability of the discrete logarithm problem in these fields is necessary for the security of bilinear pairings derived from supersingular curves with embedding degree 6 and 4 defined, respectively, over F 3 1429 and F 2 3041 ; these curves were believed to enjoy a security level of 192 bits against attacks by Coppersmith's algorithm. Our analysis shows that these pairings offer security levels of at most 96 and 129 bits, respectively, leading us to conclude that they are dead for pairing-based cryptography.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".