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An Attribute Based Access Control Framework for Healthcare System

2018· article· en· W2782392215 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Physics Conference Series · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicAccess Control and Trust
Canadian institutionsUniversity of WindsorMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAccess controlComputer scienceComputer securityMandatory access controlConfidentialityComputer access controlDiscretionary access controlRole-based access controlControl (management)Health careRendering (computer graphics)

Abstract

fetched live from OpenAlex

Nowadays, access control is an indispensable part of the Personal Health Record and supplies for its confidentiality by enforcing policies and rules to ensure that only authorized users gain access to requested resources in the system. In other words, the access control means protecting patient privacy in healthcare systems. Attribute-Based Access Control (ABAC) is a new access control model that can be used instead of other traditional types of access control such as Discretionary Access Control, Mandatory Access Control, and Role-Based Access Control. During last five years ABAC has shown some applications in both recent academic fields and industry purposes. ABAC by using user's attributes and resources, makes a decision according to an access request. In this paper, we propose an ABAC framework for healthcare system. We use the engine of ABAC for rendering and enforcing healthcare policies. Moreover, we handle emergency situations in this framework.

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)0.001
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 score0.973
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.070
GPT teacher head0.390
Teacher spread0.320 · 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