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Record W4415821109 · doi:10.1109/tnse.2025.3628214

Metaverse Hierarchical T/Key-Based Lightweight Authentication Protocol

2025· article· W4415821109 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Network Science and Engineering · 2025
Typearticle
Language
FieldComputer Science
TopicAdvanced Authentication Protocols Security
Canadian institutionsUniversity of VictoriaConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProtocol (science)PasswordAuthentication (law)Otway–Rees protocolCryptographic protocolAuthentication protocolAnonymityUniversal composabilityHash function

Abstract

fetched live from OpenAlex

Tremendous attention has been paid by researchers and developers in both industry and academia to the newly emerging network paradigm, the Metaverse. It is regarded as the next generation of fully immersive hyper-spatiotemporal virtual reality Internet. Users traverse the virtual world through head-mounted devices and are represented by avatars. However, with the rapid adoption of the metaverse, many security concerns have emerged, particularly after reports of misbehaving avatars. In this paper, we propose a secure mutual authentication protocol that allows avatars to securely authenticate and communicate with each other. To mitigate misbehaving incidents, the protocol allows traceability; hence, users of misbehaving avatars are recognized, reported, and held accountable for their actions. The protocol also provides unlinkability and anonymity for the avatars. The protocol is based on a hierarchical T/key, which is a time-based one-time password system. We formally prove the security properties of our protocol through theoretical analysis, BAN logic, and the AVISPA tool. On the other hand, the computational complexity of the verifier is one hash operation. Using a Raspberry Pi 4 Model B/8 G/Broadcom-BCM2711, Quad-core, Cortex-A72-1.5 GHz (ARM v8) 64-bit SoC processor, we computed the runtime for the protocol and compared it with other recently proposed protocols. Our comparative evaluation shows that our protocol outperforms other recently proposed 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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.267
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it