Efficient signature system using optimized elliptic curve cryptosystem over GF(2(n)).
Bibliographic record
Abstract
Elliptic curve cryptography was proposed independently by Neil Koblitz and Victor Miller in the middle of 80's. The security of Elliptic Curve Cryptography depends upon the elliptic curve discrete logarithm problem. For providing the same strength, it uses a smaller key size than that for RSA. This advantage makes it particularly suitable for some devices and applications, which have a resource constraint. Digital Signature Systems are one of the most important applications of cryptography. In Y2K IEEE has included two Elliptic Cryptography based methods in its new standard P1363. The elliptic curve cryptosystem uses "point" operations like point doubling and addition. As a consequence, optimization of, point operations plays a key role in determining the efficiency of computation. Today's technology easily permits the fabrication of multiple simple "processors" on a single chip. For such devices, a serial-parallel computation has been proposed by Adnan and Mohammad [AM03][AM03a] for a faster computation of elliptic algorithms. This thesis presents a new optimized point operations algorithm for elliptic curve cryptosystems over GF(2 n). We have designed and implemented the new algorithm for a more efficient digital signature system. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .W37. Source: Masters Abstracts International, Volume: 43-01, page: 0247. Adviser: Akshai Aggarwal. Thesis (M.Sc.)--University of Windsor (Canada), 2004.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".