MétaCan
Menu
Back to cohort
Record W2631182992 · doi:10.1109/ccece.2017.7946661

A content-based data masking technique for a built-in framework in Business Intelligence platform

2017· article· en· W2631182992 on OpenAlexaff
Osama Ali, Abdelkader Ouda

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsWestern University
Fundersnot available
KeywordsMasking (illustration)Computer scienceData warehouseBusiness intelligenceDatabaseKnowledge managementData scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The implementation of a Business Intelligence (BI) platform in different types of organizations including healthcare, has become an important project. One common implementation is to extract sensitive data from production databases and then load them into an enterprise Data Warehouse (DW). However, the internal privacy breach that occurs when developers, researchers, and testers access the DW is a serious threat that should be taken into consideration.

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)0.008
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0060.002
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 head0.679
GPT teacher head0.521
Teacher spread0.158 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations3
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicData Quality and ManagementFrench-language works237,207