DIDs-Assisted Secure Cross-Metaverse Authentication Scheme for MEC-Enabled Metaverse
Bibliographic record
Abstract
With the popularization of emerging technologies such as artificial intelligence, 5G and beyond, extended reality and blockchain, the next generation Internet is rapid expansion. “Metaverse” as an evolving paradigm of next-generation Internet, can be recognized as a fully immersive, hyper spatiotemporal and self-sustaining virtual shared space, and its concept is continuous development and evolution. It is moving from imagination to the coming reality, but it is still far from being realized. One of reasons is that distinct sub-metaverses deploying their services on heterogeneous blockchains results in major problems for interoperability, preventing the implementation of seamless integrated metaverse. Facing the challenge, this paper proposes a decentralized identifiers (DIDs) assisted secure cross-metaverse authentication scheme for MEC-enabled metaverse, which is based on a novel designed infrastructure build on MEC and blockchain. In addition, the proposed scheme adopts DIDs, which can not only achieve the secure cross-metaverse authentication, but also increase the decentralization of the metaverse. In addition, the adoption of ID-based aggregate signature can reduce the overhead of computation, communication and storage.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".