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Record W1964838363 · doi:10.1145/2613087.2613098

Access control models for geo-social computing systems

2014· article· en· W1964838363 on OpenAlexafffund
Ebrahim Tarameshloo, Philip W. L. Fong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsComputer scienceAuthorizationAccess controlKnowledge baseBase (topology)Social network (sociolinguistics)Track (disk drive)Control (management)Distributed computingComputer securityWorld Wide WebArtificial intelligenceSocial media

Abstract

fetched live from OpenAlex

A Geo-Social Computing System (GSCS) allows users to declare their current locations, and uses these declared locations to make authorization decisions. Recent years have seen the emergence of a new generation of social computing systems that are GSCSs. This paper proposes a protection model for GSCSs. The protection system tracks the current locations of users and a knowledge base of primitive spatial relations between locations. Access control policies can be formulated by the composition of primitive spatial relations. The model is extended to account for Geo-Social Network Systems (GSNSs), which track both a spatial knowledge base and a social network. A policy language for GSNSs is proposed for specifying policies that combine both social and spatial constraints.

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)0.006
metaresearch head score (Gemma)0.010
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: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0070.009
Open science0.0030.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.050
GPT teacher head0.351
Teacher spread0.300 · 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
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

Citations19
Published2014
Admission routes2
Has abstractyes

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