Access Control Policy Translation, Verification, and Minimization 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 article, 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 show that verifying whether a translated policy obeys the semantics of the original access control policy defined over a source database is intractable, even under restrictive scenarios. We then describe a practical algorithmic framework for translating relational access control policies into their XML equivalent, expressed in the eXtensible Access Control Markup Language. Finally, we examine the difficulty of minimizing translated policies, and contribute a minimization algorithm applicable to nonrecursive translated policies.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".