Multilabels-Based Scalable Access Control for Big Data Applications

Abstract

Multidata sources and formats and multiuser types introduce new security and privacy challenges to big data applications. The authors propose a multilabels-based scalable access control framework for use in a Hadoop-based big data healthcare application. This framework combines active bundle, role-based access control (RBAC), attribute-based access control (ABAC), discretionary access control (DAC), and mandatory access control (MAC). The multilabels include data type, security degree, lifetime, number of replications, access policy, and hash value. In the framework, data type can be related to security degree. If the user has new access control requirements, the big data administrator can add, delete, or revise the labels to achieve a different access control granularity.