<p>Business process mining helps organizations achieve business process discovery, monitoring, and improvement by analyzing event logs recorded by information systems. However, event logs may contain privacy information, and the analysis of event logs is at risk of privacy leakage. Many event log anonymization methods have been proposed, which mainly focus on the anonymization of event log data attributes, and investigate from the attribute level to protect the privacy information in event logs, while ignoring the impact of behavioral relationships on event log privacy, which may lead to an attacker obtaining additional information gain, and then lead to the leakage of privacy information. In order to solve this problem, this article proposes a behavioral privacy preservation method for event logs based on behavioral differential Petri nets. Combining the differential privacy technique and Petri net modeling theory, the hidden behavioral privacy requirements of event logs are analyzed through explicit attacker behavioral structure, so as to extract the behavioral relationship noise set and construct a behavioral differential Petri net model to target inter-activity behavioral relationships for privacy preservation. Experiments on synthetic and real datasets show that the method can effectively protect behavioral relationship privacy and improve the effectiveness of event log privacy preservation.</p>