Intégration du contexte spatio-temporel dans le contrôle d'accès basé sur les rôles
Bibliographic record
Abstract
L'apparition de l'informatique mobile et la democratisation des dispositifs de communications mobiles (GSM, PDA, smart gadgets) amenent les organisations a ouvrir de plus en plus leurs systemes d'information : le rendre disponible n'importe ou, n'importe quand et integrer la dimension mobile des utilisateurs. Ceci ne peut se faire sans une prise en compte reflechie de la securite des acces : un systeme d'information doit dorenavant etre capable de prendre en compte des caracteristiques contextuelles complexes (telles que la position du requerant, son profil, l'heure) pour garantir un controle d'acces fiable. Cet article a pour but de montrer que l'on peut prendre en compte des contraintes liees a la confidentialite directement dans la definition du modele logique de donnees et que cette prise en compte peut se faire de maniere homogene pour des aspects generalement geres independamment (fonction du requerant, autorite administrative, temps, position geographique, etc.). Notre proposition de langage nommee LORAAM integre l'expression d'autorisations au niveau de la classe meme, nous montrons comment mettre en oeuvre ce langage : soit par une approche de type frameworking en pre-processant LORAAM en C++, soit en reduisant ce langage dans un modele a objets integrant la notion de role.
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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".