Privacy-Preserving Authentication for Unlinkable Avatars in the Metaverse
Bibliographic record
Abstract
The metaverse is a virtual world that mirrors real life, allowing users to engage in activities and access services without the constraints of time and space. In the metaverse, users can create one or more avatars that reflect their personal preferences, enabling them to participate in activities that match their tastes and needs. To protect users’ freedom and anonymity, it is imperative for metaverse platforms to support the creation ofunlinkableavatars. This ensures that the different avatars a user creates cannot be connected, keeping their virtual identities separate and reducing the risk of retaliation for their actions. In this paper, we propose an unlinkable avatar authentication scheme, UAVA, which leverages cryptographic group signatures to enable metaverse users to create and certify their avatars without interaction with service providers. These certified avatars can then be anonymously authenticated, ensuring unlinkability between multiple avatars belonging to the same user. UAVA maintains anonymity between users and their avatars, while allowing service providers to trace malicious avatars back to their users. We formally define and prove the security properties of UAVA, and implement the protocol using socket programming, and report on its cryptographic overheads. We also evaluate its cryptographic overhead and compare it to related protocols in terms of efficiency, security, and scalability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".