Oracle Based Privacy-Preserving Cross-Domain Authentication Scheme
Bibliographic record
Abstract
The Public Key Infrastructure (PKI) system is the cornerstone of today's security communications. All users in the service domain covered by the same PKI system are able to authenticate each other before exchanging messages. However, there is identity isolation in different domains, making the identity of users in different domains cannot be recognized by PKI systems in other domains. To achieve cross-domain authentication, the consortium blockchain system is leveraged in the existing schemes. Unfortunately, the consortium blockchain-based authentication schemes have the following challenges: high cost, privacy concerns, scalability and economic unsustainability. To solve these challenges, we propose a scalable and privacy-preserving cross-domain authentication scheme called Bifrost-Auth. Firstly, Bifrost-Auth is designed to use a decentralized oracle to directly interact with blockchains in different domains instead of maintaining a consortium blockchain and enables mutual authentication for users lying in different domains. Secondly, users can succinctly authenticate their membership of the domain by the accumulator technique, where the membership proof is turned into zero knowledge to protect users' privacy. Finally, Bifrost-Auth is proven to be secure against various attacks, and thorough experiments are carried out and demonstrate the security and efficiency of Bifrost-Auth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".