RSMS: Towards Reliable and Secure Metaverse Service Provision
Bibliographic record
Abstract
Establishing and sustaining Metaverse service necessitates an unprecedented scale of resources. Some researchers consider the deployment of Metaverse service in a cloud-edge resource architecture, which can satisfy the escalating demand for Metaverse service resources while ensuring both high bandwidth and low latency. However, how to ensure the security and trustworthiness of resource nodes (RNs) in this architecture and thus ensure the reliability and security of Metaverse service is still a challenge. In this paper, we first propose a novel mechanism, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i>eliable and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i>ecure <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i>etaverse <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i>ervice (RSMS), to ensure Metaverse service reliability and security without sacrificing performance. RSMS consists of two protocols: (1) One is a blockchain-based lightweight mutual authentication protocol, which can assure the trustworthiness of heterogeneous Metaverse service RNs dynamically joining a Metaverse service resource pool and then guarantee the security of Metaverse service. (2) The other is a group authentication protocol used to form and maintain a stable and secure Metaverse service group composed of RNs, which ensures the reliability and enhances the security of Metaverse service. The reliability and security of Metaverse service under RSMS are thoroughly discussed, and also informal and formal security analysis are conducted. Additionally, we study the impact of RSMS on Metaverse service throughput, demonstrating its lightweight feature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".