Privacy-aware Process Performance Indicators: Framework and Release Mechanisms
This addresses privacy protection for organizations monitoring business processes involving individuals, but it is incremental as it builds on existing PPINOT meta-models and differential privacy techniques.
The paper tackles the problem of privacy intrusion in computing process performance indicators (PPIs) from event logs, presenting a framework with differential privacy release mechanisms that demonstrate feasibility and trade-offs between data utility and privacy in experiments.
Process performance indicators (PPIs) are metrics to quantify the degree with which organizational goals defined based on business processes are fulfilled. They exploit the event logs recorded by information systems during the execution of business processes, thereby providing a basis for process monitoring and subsequent optimization. However, PPIs are often evaluated on processes that involve individuals, which implies an inevitable risk of privacy intrusion. In this paper, we address the demand for privacy protection in the computation of PPIs. We first present a framework that enforces control over the data exploited for process monitoring. We then show how PPIs defined based on the established PPINOT meta-model are instantiated in this framework through a set of data release mechanisms. These mechanisms are designed to provide provable guarantees in terms of differential privacy. We evaluate our framework and the release mechanisms in a series of controlled experiments. We further use a public event log to compare our framework with approaches based on privatization of event logs. The results demonstrate feasibility and shed light on the trade-offs between data utility and privacy guarantees in the computation of PPIs.