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Efficient Authorization of Graph-database Queries in an Attribute-supporting ReBAC Model

机译:在属性支持Rebac模型中有效授权图数据库查询

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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 OP-Eval, a query evaluation algorithm. We also introduce Live-End Backjuinping (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.
机译:Neo4j是一个流行的图表数据库,提供两个版本:企业版和社区版本。企业版通过自定义开发的程序提供可自定义的基于角色的访问控制功能,而社区版本则不提供任何访问控制支持。作为图形数据库,Neo4j似乎是基于关系的访问控制(Rebac)的自然应用程序,该访问控制范例,其中授权决策基于系统中的主题和资源之间的关系(即,授权图)。在本文中,我们呈现了arbac,这是一个支持Neo4j的属性支持的Rebac模型,通过通过资源而不是过程来提供更精细的访问控制。 arbac采用纳米Cypher,基于Neo4J的Cypher查询语言的声明性策略语言,其结果允许我们编织具有访问控制策略的数据库查询,并同时评估。评估组合查询和策略产生结果(i)与搜索条件匹配,并且(ii)请求主题授权访问。 arbac伴随着实现所呈现的思想所需的算法及其实现,包括Op-eval,一个查询评估算法。我们还介绍了Live-End Backjuinping(LBJ),一个回溯方案,提供了对评估查询的冲突后颠覆的显着性能提升。如我们以前的工作中所示,GP-EVAL的原始版本已经比Neo4J的Cypeher评估引擎更快地执行。采用LBJ的GP-EVAL的优化版本,进一步提高了性能,从而展示了该技术的能力。

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