Event logs recorded during the execution of business processes provide a valuable starting point for process mining, and high-quality event logs can significantly enhance the quality of process mining. However, event logs often contain a substantial amount of sensitive and personal information. Therefore, the release of event logs should prioritize the model's quality while minimizing the risk of privacy exposure. Specifically, quantifying performance indicators between the original event logs and the released ones enables the operational goals. To date, privacy benefit and utility loss are two main target performance indicators, especially from the perspective of structural similarity comparison of mined process models. To the best of our knowledge, no study aims to measure the privacy-preserving performance indicators from the point of behavior differentiation between the original event logs and released ones, and this paper aims to address this problem. In this paper, we propose an approach that combines privacy benefit and utility loss measurements together to quantify the behavior differentiation between the original event logs and the corresponding released ones. Specifically, an approach of event log release mechanism that effectively combines behavior privacy gain and behavior utility loss is presented in this paper. Firstly, we discuss the challenges in scenarios where event data is released directly without any privacy preservation, and describe the various potential attacks that could occur when third-party businesses perform process mining techniques. Based on these potential attacks, we present a behavior differentiationbased event log release mechanism named PLI-Assess to combat these threats. Finally, we conduct experiments on four groups of practical event logs for comparisons with the baseline methods. The experimental results indicate general feasibility and shed light on the behavior trade-offs between privacy benefit and utility loss.
