# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 3 Methodology  
In this section, we propose and describe the goal-oriented evaluation methodology based on PPPM for possible model improvements. The proposed PPPM methodology aims to facilitate an integration of a privacy-preserving model for PM by experimenting on real-life event logs from different domains and emphasizing their individual characteristics. Figure 1 presents a comprehensive overview of the proposed goal-oriented PPPM models evaluation methodology. This methodology effectively manages substantial volumes of experimental data by systematically distributing PPPM activities, thereby extracting valuable insights through the following sequential steps:  
1. _Establishing Goals and Questions_: In the initial phase of our methodology, we employed the Goal Question Metric (GQM) framework [27] to systematically structure and guide our quantitative experimental case study, utilizing real-world event log datasets. By initially establishing well-defined, overarching goals aligned with the study's objectives, we maintained a focused and contextually relevant evaluation. Each overarching goal was systematically broken down into precise, quantifiable questions  
Figure 1: Proposed methodology. aimed at evaluating the effectiveness and robustness of the proposed methodology within realistic operational conditions. These questions were subsequently linked to well-defined metrics that furnished quantitative evidence to substantiate their corresponding answers. This systematic approach not only reinforced the rigor and traceability of the evaluation process but also promoted a transparent interpretation of results, ensuring coherence with the overarching research objectives. The resulting output of this step is the GQM table. 2. _Data Gathering_: In this step, we retrieved real-world event log datasets, accompanied by their corresponding privacy-preserving models for PM and pertinent quantitative metrics. These event logs were obtained from publicly accessible repositories to enhance reproducibility and accurately represent genuine process behaviors across a wide range of domains. Established significant privacy-preserving models are additionally gathered to evaluate their conformance with predefined metrics for PM. Consequently, the output artifacts of this phase comprise real-world event log datasets, privacy-preserving models for process mining, and quantitative metrics relevant to the associated experiments. 3. _Event Logs Description_: For each event log, we systematically identified the underlying process structure and delineated key characteristics, including variant frequency and trace uniqueness, which are essential for assessing the performance and the trade-offs between privacy and utility in our case study. This information was presented in a structured tabular format as the designated output of this phase. 4. _Data Preprocessing_: During the data preprocessing phase, feature vector normalization techniques were employed to refine the extracted metrics, ensuring their suitability for subsequent privacy-preserving machine learning model experiments. This phase was essential to guarantee the proportional contribution of all process mining features (e.g., case ids, activity names, timestamps, and other resource attributes) during model training, especially in algorithms that are sensitive to feature scaling. Standard normalization techniques, including min-max scaling, were applied based on the distribution characteristics of each event logs feature to ensure consistency and comparability. Conversely, advanced PPPM models employ proprietary trace encoding techniques internally, eliminating the necessity for an additional preprocessing layer at this stage. For these models, it is sufficient to identify the attributes, such as sensitive data and quasi-identifiers, that will be considered in the anonymization process. The outputs are the preprocessed model ready event logs representation. 5.