Metaverse-AKA: A Lightweight and PrivacyPreserving Seamless Cross-Metaverse Authentication and Key Agreement Scheme
Bibliographic record
Abstract
The recent advances of emerging technologies including artificial intelligence, 5G, 6G, extended reality and blockchain promote the rapid development of next-generation Internet. As an evolving paradigm of next-generation Internet, metaverse, a fully immersive, hyper spatiotemporal and selfsustaining virtual shared space, is moving from imagination to the coming reality. However, its massive data flow, pervasive user profiling activities and other intrinsic features can lead to a lot of security and privacy concerns, which will hinder its further deployment. Specially, since the identities of users/avatars in the metaverse can be illegally stolen, impersonated, and interoperability issues can be encountered in authentication across metaverses, this paper designs a lightweight and privacy-preserving seamless cross-metaverse authentication and key agreement scheme named MetaverseAKA to meet the challenges. Metaverse-AKA can not only realize the seamless cross-metaverse authentication but also assure the users’ privacy by achieving the anonymity and unlinkability. In addition, Metaverse-AKA also has the following advantages: (i) Realizing the traceability for users in physical world. (ii) Resistance to multiple attacks like impersonation attack, man-in-the-middle attack and replay attack. (iii) Adopting lightweight cryptographic prinitives and having better performance through experiment verification and comparison.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".