Application and Research of Algebraic Curve in Identity Authentication Security
Bibliographic record
Abstract
In the field of information security, the security of identity authentication is crucial. We focus on the application of algebraic curves in the field of identity authentication security and explore their core content in depth. Starting from the basic theory of algebraic curves, we explain the group structure and discrete logarithmic properties of elliptic curves. Then, we conduct a detailed analysis of its application mode, including key generation, encryption and decryption, and digital signature processes based on elliptic curve cryptography (ECC). This is aimed at building a secure defense line for identity authentication. Meanwhile, by using algebraic curves to construct a zero - knowledge identity authentication protocol, we can achieve identity verification under privacy protection. Compared to traditional methods, this application has obvious advantages and can effectively resist quantum computing attacks, improving efficiency with shorter key lengths and faster computation speeds. However, in the process of application promotion, there are challenges such as the urgent need to complete mathematical theories, optimize algorithms, and ensure compatibility.Conduct research on the application of algebraic curves in identity authentication security, providing both theoretical foundations and practical guidance for promoting technological innovation in this field and further enhancing the security and reliability of identity authentication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".