Access Control Matrices Research Articles

Data flow analysis from capability lists, with application to RBAC

For the analysis of access control networks, where capability lists, access control matrices, or RBAC permissions are available, it can be very useful to be able to determine which subjects can be able to know, or which objects can be able to store, data originating from objects in the network. This information can be used in order to answer questions of secrecy, integrity and privacy, related to the data flow analysis problem. On the basis of a logical method, we present a graphical formalism capable to represent such networks and for which the data flow problems can be defined. We present algorithms to calculate answers to data flow questions. Complexity analysis and simulations show that these questions can be practically answered for networks of sizes up to several tens of thousands of subjects and objects, which is the size of many real-life organizations. We also show that the results obtained can be used in the process of role engineering in Role based access control, for determining secrecy levels, as well as for eliminating or combining roles or objects. Finally, a method is demonstrated to go from capability lists to Label-based access control systems.

Role Mining with Probabilistic Models

Role mining tackles the problem of finding a role-based access control (RBAC) configuration, given an access-control matrix assigning users to access permissions as input. Most role-mining approaches work by constructing a large set of candidate roles and use a greedy selection strategy to iteratively pick a small subset such that the differences between the resulting RBAC configuration and the access control matrix are minimized. In this article, we advocate an alternative approach that recasts role mining as an inference problem rather than a lossy compression problem. Instead of using combinatorial algorithms to minimize the number of roles needed to represent the access-control matrix, we derive probabilistic models to learn the RBAC configuration that most likely underlies the given matrix. Our models are generative in that they reflect the way that permissions are assigned to users in a given RBAC configuration. We additionally model how user-permission assignments that conflict with an RBAC configuration emerge and we investigate the influence of constraints on role hierarchies and on the number of assignments. In experiments with access-control matrices from real-world enterprises, we compare our proposed models with other role-mining methods. Our results show that our probabilistic models infer roles that generalize well to new system users for a wide variety of data, while other models’ generalization abilities depend on the dataset given.