Event Log Privacy Based on Differential Petri Nets

Abstract

ABSTRACT Process mining uses event logs to improve business processes, but such logs may contain privacy information. One popular research problem is the privacy protection of event logs. Publishing logs with differential privacy is one of major research directions. Existing research achieves privacy protection primarily by injecting random noise into event logs, or merging similar information. The former ignores the fact that injecting random noise will produce apparently unreasonable activity traces, and the latter will cause a loss of process information in the process mining perspective. To solve the above problems, this article proposes a differential algorithm based on randomized response to model Petri nets for the original event logs, select the important labels in the logs by the weak sequential relationship of control flow between activities, inject noise into the Petri net model based on the important labels using the randomized response approach, and establish a differential Petri net model. Experiments on public datasets show that the event logs produced by the approach proposed do not contain unreasonable traces. Compared with the baseline approach, the proposed approach performs better on Fitness metrics with consistent privacy requirements and retains more process variants, reducing the loss of original event log process information.