Practical Aspect of Privacy-Preserving Data Publishing in Process Mining

TL;DR

This paper presents a Python-based infrastructure that integrates state-of-the-art privacy-preserving techniques into process mining, enabling practical and secure analysis of sensitive event data with comprehensive management and tracking features.

Contribution

It introduces a unified, web-based toolset for applying and managing privacy preservation techniques in process mining, bridging research and real-world application.

Findings

Supports both standard and non-standard event data

Tracks privacy modifications with explicit metadata

Provides hierarchical usage of multiple privacy techniques

Abstract

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.