  Anonymization of event logs facilitates process mining while protecting
sensitive information of process stakeholders. Existing techniques, however,
focus on the privatization of the control-flow. Other process perspectives,
such as roles, resources, and objects are neglected or subject to
randomization, which breaks the dependencies between the perspectives. Hence,
existing techniques are not suited for advanced process mining tasks, e.g.,
social network mining or predictive monitoring. To address this gap, we propose
PMDG, a framework to ensure privacy for multi-perspective process mining
through data generalization. It provides group-based privacy guarantees for an
event log, while preserving the characteristic dependencies between the
control-flow and further process perspectives. Unlike existin privatization
techniques that rely on data suppression or noise insertion, PMDG adopts data
generalization: a technique where the activities and attribute values
referenced in events are generalized into more abstract ones, to obtain
equivalence classes that are sufficiently large from a privacy point of view.
We demonstrate empirically that PMDG outperforms state-of-the-art anonymization
techniques, when mining handovers and predicting outcomes.
