  Process mining aims to provide insights into the actual processes based on
event data. These data are often recorded by information systems and are widely
available. However, they often contain sensitive private information that
should be analyzed responsibly. Therefore, privacy issues in process mining are
recently receiving more attention. Privacy preservation techniques obviously
need to modify the original data, yet, at the same time, they are supposed to
preserve the data utility. Privacy-preserving transformations of the data may
lead to incorrect or misleading analysis results. Hence, new infrastructures
need to be designed for publishing the privacy-aware event data whose aim is to
provide metadata regarding the privacy-related transformations on event data
without revealing details of privacy preservation techniques or the protected
information. In this paper, we provide formal definitions for the main
anonymization operations, used by privacy models in process mining. These are
used to create an infrastructure for recording the privacy metadata. We
advocate the proposed privacy metadata in practice by designing a privacy
extension for the XES standard and a general data structure for event data
which are not in the form of standard event logs.
