Efficient Authorization of Graph-database Queries in an Attribute-supporting ReBAC Model
Bibliographic record
Abstract
Neo4j is a popular graph database that offers two versions: an enterprise edition and a community edition . The enterprise edition offers customizable Role-based Access Control features through custom developed procedures , while the community edition does not offer any access control support. Being a graph database, Neo4j appears to be a natural application for Relationship-Based Access Control (ReBAC), an access control paradigm where authorization decisions are based on relationships between subjects and resources in the system (i.e., an authorization graph). In this article, we present AReBAC, an attribute-supporting ReBAC model for Neo4j that provides finer-grained access control by operating over resources instead of procedures. AReBAC employs Nano-Cypher, a declarative policy language based on Neo4j’s Cypher query language, the result of which allows us to weave database queries with access control policies and evaluate both simultaneously. Evaluating the combined query and policy produces a result that (i) matches the search criteria, and (ii) the requesting subject is authorized to access. AReBAC is accompanied by the algorithms and their implementation required for the realization of the presented ideas, including GP-Eval, a query evaluation algorithm. We also introduce Live-End Backjumping (LBJ), a backtracking scheme that provides a significant performance boost over conflict-directed backjumping for evaluating queries. As demonstrated in our previous work, the original version of GP-Eval already performs significantly faster than the Neo4j’s Cypher evaluation engine. The optimized version of GP-Eval , which employs LBJ, further improves the performance significantly, thereby demonstrating the capabilities of the technique.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".