Provably secure identity-based remote password registration
Bibliographic record
Abstract
One of the most significant challenges is the secure user authentication.If it becomes breached, confidentiality and integrity of the data or services may be compromised.The most widespread solution for entity authentication is the passwordbased scheme.It is easy to use and deploy.During password registration typically users create or activate their account along with their password through their verification email, and service providers are authenticated based on their Secure Sockets Layer / Transport Layer Security (SSL/TLS) certificate.We propose a certificate-less secure blind registration protocol (CLS-BPR) which is a password registration scheme based on identity-based cryptography, i.e., both the user and the service provider are authenticated by their short-lived identity-based secret key.For secure storage a bilinear map with a salt is applied, therefore in case of an offline attack the adversary is forced to calculate a computationally expensive bilinear map for each password candidate and salt that slows down the attack.New adversarial model with new secure password registration scheme are introduced.We show that the proposed protocol is based on the assumptions that solving the Bilinear Diffie-Hellman problem is computationally infeasible, the bilinear map is a one-way function, Mac is existentially unforgeable under an adaptive chosen-message attack, where the bilinear map is considered in the generic bilinear group model and the hash functions are supposed as random oracles.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".