An Abstraction-Based Approach for Privacy-Aware Federated Process Mining

Process awareness is an essential success factor in any type of business. Process mining uses event data to discover and analyze actual business processes. Although process mining is growing fast and it has already become the basis for a plethora of commercial tools, research has not yet sufficiently addressed the privacy concerns in this discipline. Most of the contributions made to privacy-preserving process mining consider an intra-organizational setting, where a single organization wants to safely publish its event data so that process mining experts can analyze the data and provide insights. However, in real-life settings, organizations need to collaborate for performing their processes, e.g., a supply chain process may involve many organizations. Therefore, event data and processes are often distributed over several partner organizations, yet organizations hesitate to share their data due to privacy and confidentiality concerns. In this paper, we introduce an abstraction-based approach to support privacy-aware process mining in inter-organizational settings. We implement our approach and demonstrate its effectiveness using real-life event logs.

Automated summary

Process awareness is crucial for business success, and process mining is a powerful tool to discover and analyze actual business processes using event data. However, privacy concerns in inter-organizational settings hinder the sharing of event data and processes between partner organizations. This paper proposes an abstraction-based approach to enable privacy-aware process mining in such settings, addressing the challenges of collaboration and confidentiality.