# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 2 Background
### Related Works  
The integration of privacy-preserving techniques into process mining has become progressively indispensable, as the examination of event logs frequently entails sensitive personal or organizational data that must be safeguarded in adherence to legal and ethical regulations. In this research progress, an initial study [3] was conducted with a descriptive analysis of PPPM research across various domains through the application of a systematic literature review methodology [14]. A total of 39 relevant papers were identified and systematically analyzed to discern demographic trends, challenges, key characteristics, and implementations pertaining to privacy in process mining. Our findings revealed that PPPM approaches have garnered substantial attention, particularly since 2018, with a primary emphasis on the anonymization of event logs rather than the advancement of privacy-compliant methodologies for process mining. Despite the inherent challenges, this field is experiencing rapid growth and remains open to further research and practical advancements. The systematic literature review further revealed that the process mining community across various domains lacks a comprehensive methodological framework that delineates the practical implementation of PPPM activities and techniques. Although numerous privacy-preserving techniques have been developed and rigorously examined within the broader domain of data mining, their direct applicability to process mining remains constrained and insufficiently explored. This necessity arises from the distinctive nature of event logs, which encompass both behavioral and contextual information that must be retained to facilitate rigorous and meaningful process analysis. Consequently, process mining presents distinct challenges in privacy preservation, necessitating the development of specialized methodologies that effectively reconcile behavioral integrity with robust privacy protections. The present work bridges this gap by introducing an innovative, goal-driven evaluation methodology expressly tailored to privacy-preserving process mining. To the best of our knowledge, no directly comparable prior studies exist; accordingly, this contribution initiates a novel research trajectory within this domain. During our literature review, we identified different PPPM approaches under different categories. We have selected state-of-the-art PPPM models from group-based anonymization and differential privacy (DP) categories for our case study. Group-based anonymized PPPM models are founded on the principle of grouping cases that share identical quasi-identifier values, based on assumptions regarding the background knowledge possessed by potential adversaries. The primary objective is to mitigate the risk of isolating a specific individual record within the event log, thereby ensuring compliance with the requisite privacy standards. \(k\)-Anonymity [15] ensures that that each record within a dataset is indistinguishable from at least \((k-1)\) other records concerning a defined set of quasi-identifiers, thereby mitigating the likelihood of re-identification. \(l\)-Diversity [16] enhances \(k\)-anonymity by mandating that each set of indistinguishable records contains a minimum of \(l\) well-represented values for sensitive attributes, thereby mitigating the risk of attribute disclosure. \(t\)-Closeness [17] enhances \(l\)-diversity by guaranteeing that the distribution of a sensitive attribute within any equivalence class remains within a specified distance \(t\) of its overall distribution in the dataset, thereby mitigating the risk of inference privacy attacks. Beyond foundational group-based anonymization models, more sophisticated group-based PPPM models are also examined to further advance the scope of exploration. The authors of paper [18] present _PRETSA_, a novel algorithm developed for event log sanitization, providing privacy guarantees through the implementation of \(k\)-anonymity and \(t\)-closeness principles.