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; a paid enterprise edition and a free community edition. The enterprise edition offers customizable Role-Based Access Control (RBAC) features through custom developed procedures, while the community edition does not offer any access control support. Being a graph database, Neo4j is 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. In this paper we present AReBAC, an attribute-supporting ReBAC model for Neo4j (applicable to both editions) that provides finer grained access control. 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 has access to. Our experiments show that our evaluation algorithm performs faster than Neo4j»s query evaluation engine when evaluating queries that are expressible using Nano-Cypher.
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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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".