Analysis of Existing Approaches and Algorithms of Post-Quantum Cryptography
Bibliographic record
Abstract
The rapidly evolving field of post-quantum cryptography necessitates a comprehensive analysis of existing technologies to determine their efficiency and reliability.This study aims to identify the strengths and weaknesses of contemporary approaches and algorithms in post-quantum cryptography, ultimately pinpointing the most effective solutions.To achieve this objective, an analytical comparison of practical post-quantum cryptography systems was conducted, considering key metrics such as security against quantum attacks, computational efficiency, compatibility with existing systems, resistance to various cyber threats, ease of implementation, potential for standardization, and compliance with regulatory requirements.The findings reveal that while numerous approaches and algorithms exist in post-quantum cryptography, the NTRU and SIKE algorithms demonstrate superior efficacy.Additionally, WOTS+, Dilithium, and SABER exhibit promising potential, each possessing unique advantages and disadvantages concerning key size, computation speed, attack resistance, and implementation feasibility.This study offers practical value by providing guidance in the selection and adoption of post-quantum cryptography technologies, thereby contributing to the field's advancement and ensuring robust security in a post-quantum era.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".