Bibliographic record
Abstract
Attribute-Based Access Control (ABAC), a promising alternative to traditional models of access control, has gained significant attention in recent academic literature. This attention has lead to the creation of a number of ABAC models including our previous contribution, Hierarchical Group and Attribute-Based Access Control (HGABAC). However, to date few complete solutions exist that provide both an ABAC model and architecture that could be implemented in real life scenarios. This work aims to advance progress towards a complete ABAC solution by introducing Hierarchical Group Attribute Architecture (HGAA), an architecture to support HGABAC and close the gap between a model and real world implementation. In addition to HGAA we also present an attribute certificate specification that enables users to provide proof of attribute ownership in a pseudonymous and off-line manner, as well as an update to the Hierarchical Group Policy Language (HGPL) to support our namespace for uniquely identifying attributes across disparate security domains. Details of our HGAA implementation are given and a preliminary analysis of its performance is discussed as well as directions for future work.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".