论文标题
学习基于属性的属性和基于关系的访问控制策略未知值
Learning Attribute-Based and Relationship-Based Access Control Policies with Unknown Values
论文作者
论文摘要
基于属性的访问控制（ABAC）和基于关系的访问控制（REBAC）提供了高度的表现力和灵活性，通过允许根据实体之间关系的属性和链条来表达策略，从而促进安全性和信息共享。从传统访问控制信息中学习ABAC和REBAC政策的算法有可能显着降低移民成本到ABAC或REBAC的成本。 本文介绍了从访问控制列表（ACL）（ACL）采矿ABAC和REBAC策略的第一个算法，以及有关实体的不完整信息，其中某些实体的某些属性值未知。我们表明，可以将这个问题的核心视为从一组包含未知数的标签特征向量中学习简洁的三值逻辑公式，并且我们给出了第一个算法（据我们所知）。
Attribute-Based Access Control (ABAC) and Relationship-based access control (ReBAC) provide a high level of expressiveness and flexibility that promote security and information sharing, by allowing policies to be expressed in terms of attributes of and chains of relationships between entities. Algorithms for learning ABAC and ReBAC policies from legacy access control information have the potential to significantly reduce the cost of migration to ABAC or ReBAC. This paper presents the first algorithms for mining ABAC and ReBAC policies from access control lists (ACLs) and incomplete information about entities, where the values of some attributes of some entities are unknown. We show that the core of this problem can be viewed as learning a concise three-valued logic formula from a set of labeled feature vectors containing unknowns, and we give the first algorithm (to the best of our knowledge) for that problem.
