# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### Data Gathering  
After the GQM table is constructed, four real-life event log datasets are extracted from the public 4 TU ResearchData repository (4 TU ResearchData: [https://data.4 tu.nl/](https://data.4 tu.nl/)). Given the algorithmic variations among PPPM models, it is essential to ensure an equitable and methodologically rigorous comparison. Therefore, we utilize the event logs with diverse structural configurations and conduct a comprehensive analysis across a wide range of privacy budget parameters, including _epsilon_ and _sigma_. The Sepsis log provides a detailed representation of hospital procedures administered to sepsis patients and encompasses a substantial number of infrequent process traces [28]. The events recorded in the BPIC13 log are associated with the VINST system, which is designed for incident and problem management [29]. CoSeLog is an event log originating from an actual customer support setting, systematically documenting detailed sequences of interactions and activities to support research in process mining and behavioral analysis [30]. The Road Traffic Fines event log documents authentic administrative procedures concerning the management of traffic fines by an Italian municipality, functioning as a benchmark dataset for research in process mining, compliance analysis, and the optimization of public sector workflows [31]. To gather privacy-preserving models, we used the respective publicly shared Python-based available code repositories in our own benchmark implementation. For the group-based privacy-preserving models, we selected AnonyPy (AnonyPy: [[https://github.com/glassonion1/anonypy](https://github.com/glassonion1/anonypy)]([https://github.com/glassonion1/anonypy](https://github.com/glassonion1/anonypy))) [32]. AnonyPy is a Python-based framework developed for the anonymization of datasets through group-based methodologies, incorporating privacy-preserving techniques such as \(k\)-anonymity, \(l\)-diversity, and \(t\)-closeness to safeguard sensitive information. It provides researchers with the capability to anonymize tabular data through the generalization or suppression of quasi-identifiers, while supporting customizable hierarchical structures and grouping methodologies. For DP-based privacy machine learning models, we employed IBM DiffPrivLib (IBM Differential Privacy Library. Available at [[https://github.com/IBM/differential-privacy-library](https://github.com/IBM/differential-privacy-library)]([https://github.com/IBM/differential-privacy-library](https://github.com/IBM/differential-privacy-library))) [33]. Developed atop widely used scientific Python libraries such as scikit-learn (scikit-learn: machine learning in Python. Available at [[https://scikit-learn.org](https://scikit-learn.org)]([https://scikit-learn.org](https://scikit-learn.org))) [34], DiffPrivLib provides DP privacy-preserving implementations of fundamental data transformation techniques, statistical functions, and machine learning algorithms. For all the other more advanced PPPM models, their own proposed code repositories were employed with recommended configurations. While _SaCoFa_ and _Laplacian-DP_ were integrated within the _PRIPEL_ framework, _TraVaS_ operates independently, maintaining its own distinct framework. For the process mining analysis component, the _PM4 Py_ (PM4 Py: Process Mining for Python. Available at [[https://github.com/process-intelligence-solutions/pm4](https://github.com/process-intelligence-solutions/pm4) py]([https://github.com/process-intelligence-solutions/pm4](https://github.com/process-intelligence-solutions/pm4) py)) process mining tool was employed to derive process control-flow representations in the form of Petri nets using the InductiveMiner algorithm [35]. Subsequently, conformance checking is conducted based on three distinct metrics as follows [36]:  
* _Fitness_: A log trace is deemed to exhibit perfect conformity with the model if it can be precisely replayed within the model and corresponds fully to a complete trace of the model.