Prof. David Basin and Prof. Joachim Buhmann win SACMAT Test of Time Award

Professors Basin and Buhmann and former doctoral student Frank are awarded for their paper "On the Definition of Role Mining”, published at SACMAT in 2010, which lays the foundations for mining access control configurations, in particular for role-based access control. Basin, Buhmann and Frank provided a comprehensive analysis of the requirements for role mining, the existing definitions of role mining, and the methods used to assess role mining results.

In their detailed study of the present definitions, algorithms, and assessment methods for role mining, the authors noted that there is a lack of consensus on goals and that present definitions fail to account for some of role mining’s practical requirements. These existing issues motivated them to propose a new definition of the role mining problem: one that fulfills the requirements that real-world enterprises typically have. In this way, they recast role mining as a prediction problem. They additionally proposed approaches suitable for solving the problem and explained methods to validate solutions.

Basin, Buhmann, and Frank will receive the Test of Time Award this upcoming June. Given the current situation with the COVID-19 pandemic, SACMAT 2020 will be held as an online event.

About Prof. David Basin:

Prof. David Basin is head of the Department of Computer Science, and a professor at the Institute of Information Security, where he leads the Information Security Group. His research focuses on Information Security, in particular on foundations, methods, and tools for modeling, building, and validating secure and reliable systems. In 2018, he was named Fellow of the ACM for his contributions to Information Security and Formal Methods. He is also founding director of ZISC, the Zurich Information Security Center, which he led from 2003-2011.

About Prof. Joachim Buhmann:

Joachim M. Buhmann is head of the Institute for Machine Learning, where he leads the Information Science and Engineering Group. Buhmann’s research covers theory and applications of machine learning and artificial intelligence, in particular to information processing in medicine and the life sciences. His conceptual and theoretical work focusses on methods, how algorithms for complex models in data science can be validated. Buhmann serves the the Swiss National Science Foundation (SNSF) as a member of the Research Council. In the year 2017, the German Pattern Recognition Society (DAGM) awarded him an honorary membership and he was elected as an individual member in the Swiss Academy of Engineering Sciences (SATW).