Towards a Security Model for Dynamic Access Control in Graph Databases
Bibliographic record
Abstract
Since databases were created and distributed, privacy and security have gained a lot of attention in the computer community.With the advancement of networks and the arrival of Cloud computing, most of the databases are publicly and remotely accessible, so they become exposed to several kinds of security threats.However, as far as we know, the existing access control security models for NoSQL databases are static, meaning the access policy remains unchanged for a given user, and does not consider his behavior during the utilization of the database.This paper presents an innovative dynamic access control model specifically designed for graph databases; the model dynamically adapts user roles based on user interactions and monitors unexpected behavior to enhance security.To handle the dynamicity aspect, we have opted for the trust concept, where we compute a given user's reputation degree according to the role he has been assigned.For every user, a trust level is computed and continually updated, according to the actions that the user performs.Based on the current trust level, the roles of the user are changed.Experimental results based on realistic scenarios show that the proposed model allows to dynamically update user roles, thus guaranteeing the security of the database.
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 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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".