Role mining with ORCA.

• Main Authors: Jürgen Schlegelmilch, Ulrike Steffens
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Published: ACM. 2005
• Subjects: Orca
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1068.6190; http://www.sis.pitt.edu/jjoshi/courses/IS2620/Spring09/schlegelmilch.pdf

ABSTRACT With continuously growing numbers of applications, enterprises face the problem of efficiently managing the assignment of access permissions to their users. On the one hand, security demands a tight regime on permissions; on the other hand, users need permissions to perform their tasks. Rolebased access control (RBAC) has proven to be a solution to this problem but relies on a well-defined set of role definitions, a role concept for the enterprise in question. The definition of a role concept (role engineering) is a difficult task traditionally performed via interviews and workshops. However, often users already have the permissions that they need to do their jobs, and roles can be derived from these permission assignments using data mining technology, thus giving the process of role concept definition a head-start. In this paper, we present the ORCA role mining tool and its algorithm. The algorithm performs a cluster analysis on permission assignments to build a hierarchy of permission clusters and presents the results to the user in graphical form. It allows the user to interactively add expert knowledge to guide the clustering algorithm. The tool provides valuable insights into the permission structures of an enterprise and delivers an initial role hierarchy for the definition of an enterprise role concept using a bottom-up approach.