A Data-Centric Approach to Quality Estimation of Role Mining Results

Role mining has been extensively used to automatically generate roles for role-based access control. Nevertheless, the two core problems in role mining, role minimization and edge concentration, are both NP-hard. While many approximate algorithms have been developed to solve the problems, experimental tests disclose that no algorithm clearly outperforms the others in both role minimization and edge concentration. The performance results highly depend on the data set under study. To determine the right role mining algorithm, a trial-and-error approach is time consuming due to the computational overhead in mining large data set. We tackle the problem from a fresh angle. Instead of developing fast role mining algorithms, we adopt a data-centric approach that quickly estimates the bounds on optimal role mining results without actually running any role mining algorithm. Based on the inherent features of the data set, the approach can also determine whether it is easy to achieve both role minimization and edge concentration, and if not, which direction, role minimization or edge concentration, that role mining could move toward further.