The Need for Being Explicit: Failed Attempts to Construct Implicit Certificates from Lattices
Bibliographic record
Abstract
Abstract Global efforts such as the National Institute of Standards and Technology (NIST)’s post-quantum standardization center on cryptographic primitives like public-key encryption and signature schemes that are secure even in the presence of quantum adversaries. In addition, one must also consider efficient certificate management as new technologies like the Internet of Things and 5G wireless networks rely on them. For example, the IEEE Standard for vehicle-to-vehicle communication depends on implicit certificates. However, the only efficient construction available is over elliptic curves, and hence not quantum-secure. This paper investigates approaches to construct implicit certificate schemes from lattices, employing the NIST Round 3 signature schemes Dilithium and Falcon. We consider emulation of the existing implicit certificate scheme and proceed to more innovative techniques like combining the two schemes or pairing them with encryption. Unfortunately, we encounter problems with each design, due to recurring causes like conflicting secret key and signature sizes, unique sampler requirements and the rigidity of the parameter sets. By explaining each of these issues, this paper will hopefully spark ideas for more successful constructions.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".