Mining roles with noisy data

There has been increasing interest in automatic techniques for generating roles for role based access control, a process known as role mining. Most role mining approaches assume the input data is clean, and attempt to optimize the RBAC state. We examine role mining with noisy input data and suggest dividing the problem into two steps: noise removal and candidate role generation. We introduce an approach to use (non-binary) rank reduced matrix factorization to identify noise and experimentally show that it is effective at identifying noise in access control data. User- and permissionattributes can further be used to improve accuracy. Next, we show that our two-step approach is able to find candidate roles that are close to the roles mined from noise-less data. This method performs better than the approach of mining noisy data directly and offering the administrator increased control in the noise removal and candidate role generation phases. We note that our approach is applicable ou...

• Type: Conference
• Year: 2010
• Where: SACMAT
• Authors: Ian Molloy, Ninghui Li, Yuan (Alan) Qi, Jorge Lobo, Luke Dickens
• Keywords: Candidate Role, Candidate Role Generation, Control Systems, Role Mining, SACMAT 2010