Bibliographic record
Abstract
Abstract. Since Diffie-Hellman [14], many secure systems, based on discrete logarithm or Diffie-Hellman assumption in Zp, were introduced in the literature. In this work, we investigate the possibility to construct efficient primitives from exponentiation techniques over Zp. Consequently, we propose a new pseudorandom generator, where its security is proven under the decisional Diffie-Hellman assumption. Our generator is the most efficient among all generators from Z ∗ p that are provably secure under standard assumptions. If an appropriate precomputation is allowed, our generator can produce O(log log p) bits per modular multiplication. This is the best possible result in the literature (even improved by such a precomputation as well). Interestingly, our generator is the first provably secure under a decisional assumption and might be instructive for discovering potentially more efficient generators in the future. Our second result is a new family of universally collision resistant hash family (CRHF). Our CRHF is provably secure under the discrete log assumption and is more efficient than all previous CRHFs that are provably secure under standard assumptions (especially without a random oracle). This result is important, especially when the unproven hash functions (e.g., MD4, MD5, SHA-1) were broken by Wang et al. [41–43]. 1
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".