# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 2 Background
### Related Works
The authors of paper [19] introduce the _TLKC_ privacy model for process mining, which provides robust privacy guarantees through the implementation of group-based anonymization techniques. Unlike _PRETSA_, introduced in paper [18], which focuses on privacy from a resource-oriented perspective, _TLKC_ prioritizes the case-based dimension of privacy within process mining. Differential privacy PPPM models are founded on the principle that the probability distribution of the data must not disclose the presence or absence of any individual case within the original event log. These models introduce random noise into the results, ensuring that variations in an individual's data do not influence the overall distribution of the original dataset. In other words, these approaches introduce uncertainty into individual data, ensuring that modifications to a single case do not influence the overall analytical outcomes. For our exploratory case study analysis under this category, we have adapted two supervised and one unsupervised DP privacy-preserving machine learning models into PM initially. As first supervised, a differentially private Naive Bayes classification model [20] integrates precisely calibrated noise into the computation of class priors and conditional probabilities, ensuring the indistinguishability of individual data records. This approach furnishes formal differential privacy assurances while preserving classification accuracy. As the second, a differentially private decision tree model [21] is constructed by introducing carefully calibrated noise into the selection process of splitting attributes or information gain metrics. This approach ensures that the presence or absence of any individual data instance does not substantially impact the structural integrity of the model, thereby offering robust differential privacy assurances. For the one unsupervised DP privacy-preserving machine learning model, we have employed state-of-the-art DP-k-Means. A DP-k-means clustering model, as introduced by [22], ensures privacy by incorporating carefully calibrated noise into both centroid updates and data point assignments throughout the iterative clustering process. This approach guarantees that the impact of any individual data record on the final cluster formation remains constrained in accordance with differential privacy principles. In addition to these, we further extended our case study experiments with more advanced DP PPPM models from the literature. Study [23] seeks to develop a comprehensive privacy protection framework for event data, grounded in the well-established principles of differential privacy. The proposed privacy protection model focuses exclusively on the abstract privacy challenge previously delineated, facilitating the dissemination of aggregate data while ensuring the confidentiality of individual participant information. Paper [24] examines the challenge of incorporating a process's semantic structure into the anonymization of control flow. The authors present _SaCoFa_ as a flexible algorithmic model designed for the controlled insertion of noise, utilizing an exponential mechanism. In study [25], the authors empirically illustrate, through a practical implementation on a real-world event log, that anonymized event logs preserve their analytical utility across a range of applications that incorporate contextual information. Namely, _PRIPEL_ guarantees differential privacy at the granularity of individual cases, rather than applying it to the event log as a whole, thereby preserving contextual information. The authors of paper [26] propose _TraVaS_, that is an innovative method for the release of differentially private trace variants, leveraging anonymized partition selection techniques to enhance privacy protection. In contrast to conventional approaches that depend on prefix expansion, often resulting in substantial computational complexity and the generation of artificial variants, _TraVaS_ prioritizes the direct publication of trace variants, thereby enhancing efficiency and authenticity.