Enabling Web Services Policy Negotiation with Privacy preserved using XACML
Bibliographic record
Abstract
In recent Web services research, there are increasing demands and discussions about negotiation technologies for different Web services applications. One of the important topics is the policy negotiation. As many business activities become automated, policy compliance negotiation between human agents can be a bottleneck. In this paper, we focus on the policy negotiation research issues in privacy policy. We adopt the extensible Access Control Markup Language (XACML) as a policy description language and explore its potential in privacy policy negotiation. We first formalize the negotiation process in the context of Web services. Then, we illustrate the policy negotiation model by introducing a policy negotiation point (PNP) between the policy enforcement point (PEP) and policy decision point (PDP) in the XACML policy management architecture. We discuss different phases in a privacy policy negotiation and finally we illustrate how PNP can help on negotiating policies through an example scenario
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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 | 0.029 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".