PRIDA-ME: A Privacy-Preserving, Interoperable and Decentralized Authentication Scheme for Metaverse Environment
Bibliographic record
Abstract
The metaverse is a new virtual world that has the potential to significantly impact our interactions with digital content and with each other. It is a shared virtual environment where users can seamlessly and with immersive experiences create, interact, and enjoy digital assets. Nevertheless, the metaverse also poses fundamental challenges, particularly about security and privacy concerns, that require careful consideration. One of the most daunting aspects of securing the metaverse is authentication. Several solutions have been proposed, including deployment of blockchain technology and smart contracts, to address these authentication challenges. While these methods provide a secure and tamper-proof authentication mechanism, they fail to meet certain critical security and privacy requirements like interoperability and decentralization. This research proposes an enhanced privacy-preserving authentication scheme based on blockchain, elliptic curve cryptography, biohashing, and a physical unclonable function that guards against various attacks. The proposed scheme does not rely on a single central authority and consists of various phases, including user and avatar authentication, password change, and avatar generation phases. The proposed scheme underwent security assessment using the Burrows Abadi Needham (BAN) logic, ProVerif tool, and Scyther tool. The results demonstrate that it provides a better level of security against a wide range of attack vectors. The proposed scheme offers a swift and efficient authentication mechanism that adheres to the requirements of the metaverse environment, such as interoperability, decentralization, and privacy protection, and requires less computation cost as compared to state-of-the-art schemes.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".