Privacy for Process Mining: A Systematic Literature Review

Process mining is an emerging scientific discipline focused on discovering, inspecting, and enhancing process models using event data collected from information systems, automating the detailed modeling work without the need for extensive manual labor. However, privacy preservation issues arise when handling such data. Although various process mining methods, models, and tools exist to support the Business Process Management life cycle, no systematic review has been conducted to evaluate these methods and models under the lens of privacy. This work aims to fill this gap by analyzing the applicability of privacy-enhanced methods for process mining. Through a systematic literature review, we identified 39 relevant papers, examining them to understand demographic trends, challenges, characteristics, and implementations related to privacy in process mining. Our findings indicate that privacy-preserving process mining approaches have gained significant attention, especially post-2018, with a predominant focus on anonymizing event logs rather than developing privacy-compliant methodologies for process mining. Most approaches aim to prevent linkage attacks while adhering to privacy regulations, with many utilizing noise addition techniques for anonymization. Despite several issues in defining and using privacy in process mining, this review provides valuable insights for researchers and practitioners, marking the first comprehensive analysis in this domain and highlighting areas for future research.