Towards Quantifying Privacy in Process Mining
This work addresses the problem of evaluating privacy preservation techniques for organizations using process mining, which is an incremental step towards more robust privacy in this domain.
This paper proposes a method to quantify the effectiveness of privacy preservation techniques in process mining by introducing two measures for disclosure risks and one measure for data utility preservation. These measures were tested on various real-life event logs.
Process mining employs event logs to provide insights into the actual processes. Event logs are recorded by information systems and contain valuable information helping organizations to improve their processes. However, these data also include highly sensitive private information which is a major concern when applying process mining. Therefore, privacy preservation in process mining is growing in importance, and new techniques are being introduced. The effectiveness of the proposed privacy preservation techniques needs to be evaluated. It is important to measure both sensitive data protection and data utility preservation. In this paper, we propose an approach to quantify the effectiveness of privacy preservation techniques. We introduce two measures for quantifying disclosure risks to evaluate the sensitive data protection aspect. Moreover, a measure is proposed to quantify data utility preservation for the main process mining activities. The proposed measures have been tested using various real-life event logs.