Access control policy translation and verification within heterogeneous data federations
Bibliographic record
Abstract
Data federations provide seamless access to multiple heterogeneous and autonomous data sources pertaining to a large organization. As each source database defines its own access control policies for a set of local identities, enforcing such policies across the federation becomes a challenge. In this paper, we first consider the problem of translating existing access control policies defined over source databases in a manner that allows the original semantics to be observed, while becoming applicable across the entire data federation. We show that such a translation is always possible, and provide an algorithm for automating the translation. We then show that verifying that a translated policy obeys the semantics of the original access control policy defined over a source database is intractable, even under restrictive scenarios. Finally, we describe a practical algorithmic framework for translating relational access control policies into their XML equivalent, expressed in the eXtensible Access Control Markup Language.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".