Further Optimizations of CSIDH: A Systematic Approach to Efficient Strategies, Permutations, and Bound Vectors.
Bibliographic record
Abstract
CSIDH is a recent post-quantum key establishment protocol based on constructing isogenies between supersingular elliptic curves. Several recent works give constant-time implementations of CSIDH along with some optimizations of the ideal class group action evaluation algorithm, including the SIMBA technique of Meyer et al. and the “two-point method” of Onuki et al. A recent work of Cervantes-Vazquez et al. details a number of improvements to the works of Meyer et al. and Onuki et al. Several of these optimizations—in particular, the choice of ordering of the primes, the choice of SIMBA partition and strategies, and the choice of bound vector which defines the secret keyspace—have been made in an ad hoc fashion, and so while they yield performance improvements it has not been clear whether these choices could be improved upon, or how to do so. In this work we present a framework for improving these optimizations using (respectively) linear programming, dynamic programming, and convex programming techniques. Our framework is applicable to any CSIDH security level, to all currently-proposed paradigms for computing the class group action, and to any choice of model for the underlying curves. Using our framework we find improved parameter sets for the two major methods of computing the group action: in the case of the implementation of Meyer et al. we obtain a 13.04% speedup without applying the further optimizations proposed by Cervantes-Vazquez et al., while for that of Cervantes-Vazquez et al. under the two-point method we obtain a speedup of 5.23%, giving the fastest constant-time implementation of CSIDH to date.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".