Fine‐grained filtering to provide access control for data providing services within collaborative environments
Bibliographic record
Abstract
Summary A data providing service (DPS) in service‐oriented architecture is tasked only with the retrieval of data that are annotated over a domain ontology. One particular motivating application of DPSs is their use within collaborative environments. An important characteristic for the enterprises of such a collaborative environment is the ability to employ data sharing with one another. A major concern in this situation is the protection of each enterprise's privacy while still permitting data sharing. One potential solution is to provide filtered data through access control. This work describes how to implement access control through fine‐grained filtering of DPS response messages; it is accomplished using a filtering ontology and relations between the domain ontology of DPS and the proposed filtering ontology. Therefore, enterprises can write enterprise‐specific access control policies referencing a common filtering ontology defined within a collaborative environment, enabling access control‐based data sharing within the environment. This work additionally illustrates the implementation of our general solution to data providing web services, interpreted by an eXtensible Access Control Markup Language‐based access control framework. The implementation is further evaluated in a case study of real world data, provided by a health research institute in London, Canada. Copyright © 2013 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".