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Record W2197800877 · doi:10.1145/2841113.2841121

Towards Managed Role Explosion

2015· article· en· W2197800877 on OpenAlexaff
Aaron Elliott, Scott Knight

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsRole-based access controlPermissionComputer scienceAccess controlScalabilityComputer securityDecoupling (probability)DatabaseEngineering

Abstract

fetched live from OpenAlex

Role-based access control (RBAC) is a popular framework for securing information systems in medium to large organizations with hundreds or thousands of employees. However, very few descriptions of existing RBAC systems can be found in the literature. In this paper, we challenge the belief, notion or sense that the number of subjects far exceeds the roles found in enterprise systems. First, we analyze the RBAC system found at ACME University, comparing it to a recently introduced fragment of RBAC called bi-sorted role-based access control (RBÄC). Then we investigate how ACME performs access management, using our new hierarchical graphing model to better visualize the subject-permission mappings. Next, we present our results and introduce a new role-centric methodology for dynamically constraining access to information. Finally, we describe how organizational scalability is enhanced at ACME University by decoupling subject and permission management at the expense of managed role explosion.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.779

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 communication0.0000.000
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 head0.055
GPT teacher head0.323
Teacher spread0.268 · 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 designNot applicable
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

Citations8
Published2015
Admission routes1
Has abstractyes

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