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Post-Quantum Based Oblivious Transfer for Authentication in Heterogeneous Internet of Everything

2023· article· en· W4387870310 on OpenAlexaff
Ashwin Balaji, Sanjay Kumar Dhurandher, Karan Gupta, Isaac Woungang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceOblivious transferAuthentication (law)EncryptionProtocol (science)Computer securityComputer networkResilience (materials science)Quantum computerThe InternetQuantumAuthentication protocolDistributed computingCryptographyOperating system

Abstract

fetched live from OpenAlex

Quantum security in multi-party authentication is a critical aspect to be addressed in Internet of Everything (IoE), especially when considering the possible future network attacks by the usage of quantum computers. To ensure an invulnerable multi-party computation, the Post-quantum based Oblivious Transfer for authentication in heterogeneous internet of everything Systems (POTS) protocol proposed in this paper integrates 1-out-n oblivious transfer along with the post-quantum encryption to preserve the network from suspicious parties during communication. The protocol further intends to evaluate resilience by considering various metrics namely, hit ratio, execution time and memory consumption, in order to attain considerable accuracy in an IoE environment. The POTS protocol achieves an execution time of 66.69 seconds, device memory consumption of 131.95 MB and hit ratio of 20.46% for the session size of 300.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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