Behaviour Anonymous Method of Business Process Based on Log Skeleton

Abstract

Privacy-preserving process mining (PPPM) is a technology that analyses and optimises processes while safeguarding sensitive information. Earlier research on PPPM mainly focused on the data perspective, employing techniques such as noise insertion and data generalisation to protect the sensitive personal information of process executors. However, these studies overlooked the behavioural relationships between activities in the process. Attackers can exploit domain knowledge and certain behavioural information of executors to carry out re-identification attacks, leading to the leakage of personal sensitive information. To address this issue, a behaviour anonymous method of business process based on a log skeleton is proposed. This method begins with the individual process model of the executor, clustering based on the similarity between models and utilising K-anonymity and the log skeleton technology to achieve cluster division and the construction of behaviour constraint sets. Furthermore, the generation of privacy models is standardised by the behaviour constraint set to achieve global behaviour anonymization. To evaluate the effectiveness of this method, experiments were conducted using multiple real and synthetic datasets. The experimental results indicate that this method significantly outperforms the comparison methods.