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Record W2566006170 · doi:10.1145/3007204

Current Research and Open Problems in Attribute-Based Access Control

2017· review· en· W2566006170 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM Computing Surveys · 2017
Typereview
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsWestern University
Fundersnot available
KeywordsComputer scienceAccess controlRole-based access controlScalabilityComputer securityDelegationControl (management)Discretionary access controlData scienceWorld Wide WebDatabaseArtificial intelligencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Attribute-based access control (ABAC) is a promising alternative to traditional models of access control (i.e., discretionary access control (DAC), mandatory access control (MAC), and role-based access control (RBAC)) that is drawing attention in both recent academic literature and industry application. However, formalization of a foundational model of ABAC and large scale adoption is still in its infancy. The relatively recent emergence of ABAC still leaves a number of problems unexplored. Issues like delegation, administration, auditability, scalability, hierarchical representations, and the like, have been largely ignored or left to future work. This article provides a basic introduction to ABAC and a comprehensive review of recent research efforts toward developing formal models of ABAC. A taxonomy of ABAC research is presented and used to categorize and evaluate surveyed articles. Open problems are identified based on the shortcomings of the reviewed works and potential solutions discussed.

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.

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)0.040
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0400.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0040.001
Open science0.0080.003
Research integrity0.0000.001
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 head0.674
GPT teacher head0.603
Teacher spread0.070 · 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