The welch-gong stream cipher - evolutionary path
Bibliographic record
Abstract
Abstract This survey presents the rich history of the Welch-Gong (WG) Stream cipher family. It has been a long journey that lead the WG stream ciphers to become practical. The evolutionary path is a combination of mathematical endeavour and engineering striving to transfer pure mathematical functions to practical encryption algorithms for various applications. This path began as the pioneering work on WG transformation sequences with 2-level autocorrelation, leading to important breakthroughs in the early 2000’s, such as the submission of the first WG stream cipher to the eSTREAM competition in 2005 and the subsequent introduction of the WG stream cipher family WG(m, l), followed by extensive work on particular instances proposed for various (mostly lightweight) applications. A recent construction using a WG permutation is the authenticated encryption WAGE, submitted to the NIST LWC competition in 2019. The story of the WG stream cipher is by far not finished. The future opens numerous possibilities for WG stream ciphers and WAGE, with applications in both lightweight environments and in high-performance computing. We conclude the survey with new ideas and open problems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".