Administrative Role-Based Access Control Policies Research Articles

Policy analysis for administrative role based access control without separate administration

Role based access control (RBAC) is a widely used approach to access control with well-known advantages in managing authorization policies. This paper considers user-role reachability analysis of administrative role based access control (ARBAC), which defines administrative roles and specifies how members of each administrative role can change the RBAC policy. Most existing works on user-role reachability analysis assume the separate administration restriction in ARBAC policies. While this restriction greatly simplifies the user-role reachability analysis, it also limits the expressiveness and applicability of ARBAC. In this paper, we consider analysis of ARBAC without the separate administration restriction and present new techniques to reduce the number of ARBAC rules and users considered during analysis. We also present parallel algorithms that speed up the analysis on multi-core systems. The experimental results show that our techniques significantly reduce the analysis time, making it practical to analyze ARBAC without separate administration.

Scalable automated symbolic analysis of administrative role-based access control policies by SMT solving1

Administrative Role Based Access Control ARBAC is one of the most widespread framework for the management of access-control policies. Several automated analysis techniques have been proposed to help maintaining desirable security properties of ARBAC policies. One of the main limitation of available analysis techniques is that the set of users is bounded. In this paper, we propose a symbolic framework to overcome this limitation. We design an automated analysis technique that can handle both a bounded and an unbounded number of users by adapting recent methods for the symbolic model checking of infinite state systems that use first-order logic and SMT solving techniques. An extensive experimental evaluation confirms the scalability of the proposed technique.

Policy analysis for Administrative Role-Based Access Control

Role-Based Access Control (RBAC) is a widely used model for expressing access control policies. In large organizations, the RBAC policy may be collectively managed by many administrators. Administrative RBAC (ARBAC) models express the authority of administrators, thereby specifying how an organization’s RBAC policy may change. Changes by one administrator may interact in unintended ways with changes by other administrators. Consequently, the effect of an ARBAC policy is hard to understand by simple inspection. In this paper, we consider the problem of analyzing ARBAC policies. Specifically, we consider reachability properties (e.g., whether a user can eventually be assigned to a role by a group of administrators), availability properties (e.g., whether a user cannot be removed from a role by a group of administrators), containment properties (e.g., every member of one role is also a member of another role) satisfied by a policy, and information flow properties. We show that reachability analysis for ARBAC is PSPACE-complete. We also give algorithms and complexity results for reachability and related analysis problems for several categories of ARBAC policies, defined by simple restrictions on the policy language. Some of these results are based on the connection we establish between security policy analysis and planning problems in Artificial Intelligence.

Symbolic reachability analysis for parameterized administrative role-based access control

Role-based access control (RBAC) is a widely used access control paradigm. In large organizations, the RBAC policy is managed by multiple administrators. An administrative role-based access control (ARBAC) policy specifies how each administrator may change the RBAC policy. It is often difficult to fully understand the effect of an ARBAC policy by simple inspection, because sequences of changes by different administrators may interact in unexpected ways. ARBAC policy analysis algorithms can help by answering questions, such as user-role reachability, which asks whether a given user can be assigned to given roles by given administrators. Allowing roles and permissions to have parameters significantly enhances the scalability, flexibility, and expressiveness of ARBAC policies. This paper defines PARBAC, which extends the classic ARBAC97 model to support parameters, proves that user-role reachability analysis for PARBAC is undecidable when parameters may range over infinite types, and presents a semi-decision procedure for reachability analysis of PARBAC. To the best of our knowledge, this is the first analysis algorithm specifically for parameterized ARBAC policies. We evaluate its efficiency by analyzing its parameterized complexity and benchmarking it on case studies and synthetic policies. We also experimentally evaluate the effectiveness of several optimizations.