A Model Study on Collaborative Learning and Exploration of RBAC Roles

Abstract

Role-based access control (RBAC) can effectively guarantee the security of user system data. With its good flexibility and security, RBAC occupies a mainstream position in the field of access control. However, the complexity and time-consuming of the role establishment process seriously hinder the development and application of the RBAC model. The introduction of the assistant interactive question answering algorithm based on attribute exploration (semiautomatic heuristic way to build an RBAC system) greatly reduces the complexity of building a role system. However, there are some defects in the auxiliary interactive Q&A algorithm based on attribute exploration. The algorithm is not only unable to support multiperson collaborative work but also difficult to find qualified Q&A experts in practical work. Aiming at the above problems, this paper proposes a model collaborative learning and exploration of RBAC roles under the framework of attribute exploration. In this model, after interactive Q&A with experts in different permissions systems by using attribute exploration, the obtained results are merged and calculated to get the correct role system. This model not only avoids the time-consuming process of role requirement analysis but also provides a feasible scheme for collaborative role discovery in multidepartment permissions.