Verifiable Obtained Random Subsets for Improving SPHINCS
Bibliographic record
Abstract
SPHINCS+ is a stateless hash-based digital signature scheme and an alternate candidate in round 3 of the NIST Post-Quantum Cryptography standardization competition. Although not considered as a finalist because of its performance, SPHINCS+may be considered for standardization by NIST after another round of evaluations. In this paper, we propose a Verifiable Obtained Random Subsets (v-ORS) generation mechanism which with one extra hash computation binds the message with the signing FORS instance (the underlying few-time signature algorithm). This enables SPHINCS+ to offer more security against generic attacks because the proposed modification restricts the ORS generation to use a hash key from the utilized signing FORS instance. Consequently, such a modification enables the exploration of different parameter sets for FORS to achieve better performance at the same security level. For instance, when using v-ORS, one parameter set for SPHINCS+-256s provides 82.9% reduction in the computation cost of FORS which leads to around 27% reduction in the number of hash calls of the signing procedure. Given that NIST has identified the performance of SPHINCS+ as its main drawback, these results are a step forward in the path to standardization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 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 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".