Efficient Ring Signature and Group Signature Schemes Based on q-ary Identification Protocols
Bibliographic record
Abstract
While designing ring signature and group signature is a relatively mature area, few published schemes are both efficient and quantum attack-resilience. In this paper, we present two new signature schemes based on coding theory. First, we present two new zero-knowledge (ZK) identification protocols based on the construction of (Cayrel, P.L., Véron, P. and Alaoui, S.M.E.Y. (2010) A Zero-Knowledge Identification Scheme Based on the q-ary Syndrome Decoding Problem. Proceedings of SAC 2010, Waterloo, Ontario, Canada, August 12–13, pp. 171–186. Springer, Berlin) in order to improve efficiency of code-based digital signature schemes. We then transform the newly proposed ZK protocols into a ring signature scheme and a group signature scheme. Our schemes enjoy a significant improvement in efficiency since reducing the cheating probability decreases the interaction rounds. Specially, with the security level of 2−87, the sizes of public key and signature are 14.5 KB and 52 KB in our ring signature scheme, while the corresponding sizes are 400 KB and 2384 KB in the scheme of (Cayrel, P. L., Alaoui, S. M. E. Y., Hoffmann, G. and Véron, P. (2012) An improved threshold ring signature scheme based on error correcting codes. Proceedings of WAIFI 2012, Bochum, Germany, July 16–19, pp. 45–63. Springer, Berlin). At the security level of 2−80, the sizes of public key and signature are 32 KB and 113.8 KB in our group signature scheme, as compared with 2.5 MB and 20 MB in the scheme of (Alamélou, Q., Blazy, O., Cauchie, S. and Gaborit, P. (2017) A codebased group signature scheme. Des. Codes Cryptogr., 82, 469–493) and 642 KB and 114 KB in the scheme of (Ezerman, M.F., Lee, H.T., Ling, S., Nguyen, K. and Wang, H. (2015) A provably secure group signature scheme from code-based assumptions. Proc. ASIACRYPT 2015, Auckland, New Zealand, November 29–December 3, pp. 260–285. Springer, Berlin).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".