Accelerating EdDSA Signature Verification with Faster Scalar Size Halving
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
This paper establishes that the extended Euclidean algorithm (EEA) implemented in a division-free manner is faster than the Lagrange algorithm with a similar level of optimization when it comes to halving the size of scalars found in the equations of elliptic curve signature verification. Our implementation results show that our EEA based method achieves roughly 4x speed-up for generating half- size scalars used in EdDSA. For the first time ever, EEA generated half-size scalars are used for verification of individual Ed25519 signatures yielding timing results that outperform ed25519-donna, a highly optimized open source implementation, by 16.12%. We also propose a new randomization method applied with half-size scalars to batch verification of Ed25519 signatures for which we report speed-ups compared to the well-known Bernstein et al. method for batch sizes larger than six, specifically, our method achieves 11.60% improvement for batch size 64.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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