# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 1 Introduction  
Process mining is an evolving academic discipline dedicated to the systematic identification, monitoring, and optimization of processes through the extraction and analysis of data from event logs generated by information systems [1]. Its primary objective is to enhance the comprehension of real-world processes, serving as a crucial enabler of Automated Business Process Management. The extraction and analysis of event logs represents the foundational phase of process mining, yet this stage is often accompanied by various challenges and complexities. Due to the inherent characteristics of information flow within information systems, event logs may contain noise and inconsistencies, potentially affecting the accuracy and reliability of process analysis. Conversely, the process models derived through mining may not accurately reflect real-world operational dynamics and thus require refinement and enhancement by managerial oversight. Process mining techniques effectively address these challenges, ultimately yielding precise and high-quality process models. Event logs often contain readily accessible and sensitive personal information, necessitating careful handling to ensure data privacy. Concerns regarding privacy preservationemerge whenever such data are incorporated through any mechanism. In accordance with regulatory frameworks such as the European General Data Protection Regulation (GDPR) [2], process analysts are obligated to account for individuals' privacy in their analyses and decision-making processes. Recently, a diverse array of methodologies has been utilized to analyze event logs within the framework of process mining. However, the influence of privacy regulations on both the technical infrastructure and organizational dimensions of process mining has revealed a substantial gap that necessitates careful examination and strategic remediation. Building upon the identified necessity and a recent literature review conducted by the same researchers [3], this article introduces a novel goal-oriented evaluation methodology aimed at enhancing PPPM models for diverse domains. This goal-driven methodology seeks to enhance privacy-preserving models by integrating domain knowledge into process enactments, implementing privacy-related activities and techniques within the PM framework, and offering practical guidance to ensure the traceability, usability, and functionality of PPPM implementations. This methodology commences with the delineation of its scope, followed by the development of a structured table known as GQM, which systematically defines the initial project goals (G), corresponding questions (Q), and relevant metrics (M) within the context of a PPPM project. In the subsequent phase, the extraction of event log data and corresponding models followed by their description is conducted in alignment with predefined goals and guiding questions. Subsequently, the data undergo preprocessing through modeling representation, establishing a structured framework for their interpretation in accordance with the predefined goals. Building upon the GQM table, the PPPM analysis phase is conducted, wherein the performance metrics associated with each question are systematically evaluated. The obtained results, including performance indicators and responses to the formulated questions, serve as inputs for the subsequent phase, which focuses on assessing the utility and effectiveness of PPPM models. Ultimately, potential enhancement opportunities for PPPM models are systematically identified and recommended. The research presented in this paper was conducted in accordance with a design science methodology [4], wherein artifacts were systematically developed and assessed to address the identified requirements. In this study, the term \"artifact\" denotes a goal-oriented evaluation methodology, with the research structured into two distinct phases: the design and construction phase, followed by the evaluation phase, wherein the validity of the artifact is assessed through a case study.