MétaCan
Menu
Back to cohort
Record W2020043802 · doi:10.1109/pimrc.2011.6139693

Location-based anonymous authentication for vehicular communications

2011· article· en· W2020043802 on OpenAlexaff
Subir Biswas, Jelena Mišić

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsToronto Metropolitan UniversityUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceAuthentication (law)AnonymityCertificatePublic-key cryptographyElliptic Curve Digital Signature AlgorithmComputer securityScheme (mathematics)Vehicular ad hoc networkMessage authentication codeComputer networkKey (lock)Elliptic curve cryptographySignature (topology)CryptographyWirelessEncryptionWireless ad hoc networkTelecommunicationsTheoretical computer science

Abstract

fetched live from OpenAlex

We present an anonymous authentication scheme for vehicular networks, that provides conditional anonymity to collocated vehicles. A modified ECDSA mechanism utilizes the position information of vehicles operating together in close proximity for generation and verification of elliptic curve-based signatures on safety and other application messages. This waives the requirement of a third party public-key certificate for message authentication in VANET. Our scheme provides a privacy-preserving, lightweight, secure, and compatible instant authentication for vehicle-originated safety messages. Security analysis and simulation experiments justify the usefulness of our scheme.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.036
GPT teacher head0.226
Teacher spread0.190 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations15
Published2011
Admission routes1
Has abstractyes

Explore more

Same topicVehicular Ad Hoc Networks (VANETs)French-language works237,207