KAPUER : un assistant à l’écriture de politiques d’autorisation pour la protection de la vie privée
Bibliographic record
Abstract
Nous utilisons de plus en plus d’équipements informatique connectés à Internet. Nos téléphones, nos tablettes, et maintenant les équipements de notre quotidien peuvent désormais partager des informations pour faciliter notre vie. Partager ces données peut porter préjudice à notre vie privée et il est nécessaire de les contrôler. Cependant, cette tâche est complexe surtout pour des utilisateurs novices. Pour les y aider, nous présentons un système d’aide à la décision appelé KAPUER dont l’objectif est d’apprendre les préférences en termes de protection de la vie privée et de proposer des règles adaptées pour le contrôle de l’accès aux données. Ce système est intégré dans l’architecture de gestion d’autorisation XACML et trois algorithmes d’apprentissages sont évalués. \n \nWe are using more and more devices connected to the Internet. Our smartphones, tablets and now everyday items can share data to make our life easier. Sharing data may harm our privacy and there is a need to control them. However, this task is complex especially for non technical users. To facilitate this task, we present a decision support system, named KAPUER, that proposes high level authorization policies by learning users’ privacy preferences. KAPUER has been integrated into XACML and three learning algorithms have been evaluated.
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How this classification was reachedexpand
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".