Delegation of access rights in a privacy preserving access control model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it