Privacy-Preserving Data Publishing in Process Mining

TL;DR

This paper addresses the challenge of balancing privacy preservation and data utility in process mining by proposing formal definitions for anonymization operations, a privacy metadata infrastructure, and extending data standards.

Contribution

It introduces formal definitions for anonymization in process mining, a privacy metadata infrastructure, and a privacy extension for the XES standard, enhancing privacy-aware data publishing.

Findings

Formalized anonymization operations for process mining

Designed a privacy metadata recording infrastructure

Extended XES standard for privacy-aware event data

Abstract

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.