Mining Roles from Event Logs While Preserving Privacy

Process mining aims to provide insights into the actual processes based on event data. These data are widely available and often contain private information about individuals. On the one hand, knowing which individuals (known as resources) performed specific activities can be used for resource behavior analyses like role mining and is indispensable for bottleneck analysis. On the other hand, event data with resource information are highly sensitive. Process mining should reveal insights in the form of annotated models, but should not reveal sensitive information about individuals. In this paper, we show that the problem cannot be solved by naïve approaches like encrypting data, and an anonymized person can still be identified based on a few well-chosen events. We, therefore, introduce a decomposition method and a collection of techniques that preserve the privacy of the individuals, yet, at the same time, roles can be discovered and used for further bottleneck analyses without revealing sensitive information about individuals. To evaluate our approach, we have implemented an interactive environment and applied our approach to several real-life and artificial event logs.