The Impact of Event Data Partitioning on Privacy-aware Process Discovery

2025-07-08

AI Summary

To address the challenge of balancing privacy preservation and process discovery utility in highly complex event logs, this paper proposes a privacy-enhancing method integrating event abstraction with partitioned anonymization. First, event abstraction semantically partitions the original log into coherent sub-logs. Second, each sub-log undergoes k-anonymization based on the Directly-Follows Relation (DFR), applied independently to preserve local behavioral patterns. Finally, Alpha Algorithm and Inductive Miner are employed to assess the fidelity of the reconstructed process models. Experiments on three real-world event logs demonstrate that, while satisfying k-anonymity, the method significantly improves structural accuracy and behavioral coverage of discovered models compared to conventional global anonymization approaches. It effectively mitigates utility loss inherent in global methods and achieves a substantial improvement in the privacy–utility trade-off.

Technology Category

• Machine Learning: Privacy
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Knowledge Representation and Reasoning: Description Logics

Application Category

• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Information systems support the execution of business processes. The event logs of these executions generally contain sensitive information about customers, patients, and employees. The corresponding privacy challenges can be addressed by anonymizing the event logs while still retaining utility for process discovery. However, trading off utility and privacy is difficult: the higher the complexity of event log, the higher the loss of utility by anonymization. In this work, we propose a pipeline that combines anonymization and event data partitioning, where event abstraction is utilized for partitioning. By leveraging event abstraction, event logs can be segmented into multiple parts, allowing each sub-log to be anonymized separately. This pipeline preserves privacy while mitigating the loss of utility. To validate our approach, we study the impact of event partitioning on two anonymization techniques using three real-world event logs and two process discovery techniques. Our results demonstrate that event partitioning can bring improvements in process discovery utility for directly-follows-based anonymization techniques.

Problem

• Balancing privacy and utility in event log anonymization
• Reducing utility loss in complex event log anonymization
• Improving process discovery via partitioned event log anonymization

Innovation

• Combines anonymization with event data partitioning
• Uses event abstraction for log segmentation
• Improves utility in directly-follows-based anonymization