  Process mining techniques such as process discovery and conformance checking
provide insights into actual processes by analyzing event data that are widely
available in information systems. These data are very valuable, but often
contain sensitive information, and process analysts need to balance
confidentiality and utility. Privacy issues in process mining are recently
receiving more attention from researchers which should be complemented by a
tool to integrate the solutions and make them available in the real world. In
this paper, we introduce a Python-based infrastructure implementing
state-of-the-art privacy preservation techniques in process mining. The
infrastructure provides a hierarchy of usages from single techniques to the
collection of techniques, integrated as web-based tools. Our infrastructure
manages both standard and non-standard event data resulting from privacy
preservation techniques. It also stores explicit privacy metadata to track the
modifications applied to protect sensitive data.
