Attack on ``Strong Diffie-Hellman-DSA KE" and Improvement
Bibliographic record
Abstract
In this paper, we do a cryptanalyse of the so called "Strong Diffie-Hellman-DSA Key Exchange (briefly: SDH-DSA-KE)" and after we propose "Strong Diffie-Hellman-Exponential-Schnnor Key Exchange (briefly: SDH-XS-KE)" which is an improvement for efficiency and security. SDH-XS-KE protocol is secure against Session State Reveal (SSR) attacks, Key independency attacks, Unknown-key share (UKS) attacks and Key-Compromise Impersonation (KCI) attacks. Furthermore, SDH-XS-KE has Perfect Forward Secrecy (PFS) property and a key confirmation step. The new proposition is not vulnerable to Disclosure to ephemeral or long-term Diffie-Hellman exponents. We design our protocol in finite groups therefore this protocol can be implemented in elliptic curves.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".