Process mining enables business owners to discover and analyze their actual
processes using event data that are widely available in information systems.
Event data contain detailed information which is incredibly valuable for
providing insights. However, such detailed data often include highly
confidential and private information. Thus, concerns of privacy and
confidentiality in process mining are becoming increasingly relevant and new
techniques are being introduced. To make the techniques easily accessible, new
tools need to be developed to integrate the introduced techniques and direct
users to appropriate solutions based on their needs. In this paper, we present
a Python-based infrastructure implementing and integrating state-of-the-art
privacy/confidentiality preservation techniques in process mining. Our tool
provides an easy-to-use web-based user interface for privacy-preserving data
publishing, risk analysis, and data utility analysis. The tool also provides a
set of anonymization operations that can be utilized to support
privacy/confidentiality preservation. The tool manages both standard XES event
logs and non-standard event data. We also store and manage privacy metadata to
track the changes made by privacy/confidentiality preservation techniques.

By admin