Towards a base ontology for privacy protection in service-oriented architecture
Bibliographic record
Abstract
The service consumer's confidence in the protection of their privacy is an important factor for the success of electronic services (e-services). It may increase if the service provider offers a description of its data practices. This description can be compared to what the consumer defines as appropriate practices. To allow the exchange of privacy-related descriptions and automatically compare them, the parties involved in the interaction should be able to use a common vocabulary. The goal of this paper is to present a base privacy ontology for e-services and a privacy framework for service-oriented architecture (SOA). The ontology offers a base vocabulary that can be extended to create ontologies specific to a given service domain and operating environment. The framework uses ontologies so that it can support service selection considering the consumer's privacy requirements. It extends SOA with provider policies and consumer preferences based on privacy ontologies.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".