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Record W2168586728 · doi:10.1109/cscwd.2009.4968090

Zero-knowledge trust negotiation

2009· article· en· W2168586728 on OpenAlexafffund
Bo Wang, Ruizhong Wei

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCredentialComputer scienceAccess controlComputer securityDatabase transactionAuthentication (law)Service providerZero-knowledge proofService (business)Credit cardPublic key certificateNegotiationFlexibility (engineering)Protocol (science)The InternetInformation leakageComputer networkCryptographyWorld Wide WebPublic-key cryptographyBusiness

Abstract

fetched live from OpenAlex

Electronic business or on-line cooperation transactions happen regularly over the internet. Such a transaction usually involves a service provider who provides a certain service (i.e., perform an on-line purchase) and a service requester who requests the service. In order to decide whether a service requester can access a service, a distributed access control system can be used. Traditional identity-based access control systems usually require pre-register, which is too rigid to adapt to the rapid developing on-line cooperation. Trust-based access control provides open authentication and access control. The flexibility that it introduces could boost the on-line cooperation significantly. However, it is vulnerable to attacks that lead to leakage of sensitive information. Furthermore, certain credentials (such as, credit card number) are too sensitive to release for some people even through proper release policies. This paper introduces the Zero-knowledge protocol for credential verification, and presents a trust-based access control framework that incorporates this protocol. This system keeps the highly sensitive credentials secret; while at the same time proceed with the trust negotiation.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.249
Teacher spread0.237 · 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 teacher head, not a consensus.

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

Citations1
Published2009
Admission routes2
Has abstractyes

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