CABAC: A Content-Driven Attribute-Based Access Control Model for Big Data

Abstract

In recent years, attribute-based access control (ABAC) models have been widely used in big data and cloud computing. However, with the growing importance of data content, using data content to assist authorization for access controls has become more common. In this paper, we propose a dynamic content-driven attribute-based access control model (CABAC) for large-scale unstructured data. CABAC is a fine-grained access control model that use two-layer authorization to balance efficiency and accuracy. The first-layer authorization uses attributes to grant users basic authority and the second-layer authorization uses data content to grant broader authority over “related” data. Experimental results show that CABAC has acceptable efficiency and it can expand the authority of users without reducing security.