Delegation of access rights in a privacy preserving access control model
Bibliographic record
Abstract
Delegation is a process of sharing access rights by users of an access control model. It facilitates the distribution of authorities in the model. It is also useful in collaborative environments. Despite the advantages, delegation may have an impact on the access control model's security. Allowing users to share access rights without the control of an administrator can be used by malicious users to exploit the model. Delegation may also result in privacy violations if it allows accessing data without the data provider's consent. Even though the consent is taken, the privacy can still be violated if the data is used differently than the data provider agreed. Our work investigates data privacy in delegation. As a contribution, a privacy model is introduced that allows a data provider setting privacy policies that state how their data should be used by different organizations or parties who are interested in their data. Based on this setting, a delegation model is designed to consider the privacy policies in taking delegation decisions and also, to set the data usage criteria for the access right receivers. In addition to privacy policies, several delegation policies and constraint have been used to control delegation operations. Delegation is studied within a party and between two parties.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".