Bibliographic record
Abstract
The problem of learning access control policies is receiving increasing attention in research. We contribute to the foundations of this problem by posing and addressing meaningful questions on computational hardness. Our work addresses learning access control policies in the context of three different models from the literature: the access matrix, and Role- and Relationship-Based Access Control (RBAC and ReBAC, respectively). Our underlying theory is the well-established notion of Probably Approximately Correct (PAC), with careful extensions for our setting. The data, or examples, a learning algorithm is provided in our setup is that related to access enforcement, which is the process by which a request for access to a resource is decided. For the access matrix, we pose a learning problem that turns out to be computationally easy, and another that we prove is computationally hard. We generalize the former result so we have a sufficient condition for establishing other problems to be computationally easy. With these results as the basis, we consider five learning problems in the context of RBAC, two of which turn out to be computationally hard. Finally, we consider four learning problems in the context of ReBAC, all of which turn out to be computationally easy. Every proof for a problem that is computationally easy is constructive, in that we propose a learning algorithm for the problem that is efficient, and probably, approximately correct. As such, our work makes contributions at the foundations of an important, emerging aspect of access control, and thereby, information security.
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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 | 0.010 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| 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; 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".