# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## Abbreviations
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Event Log** & **Metric** & **Mean\_Diff** & **p\_lower** & **p\_upper** & **Eq.** \\ \hline LaplacianDP & Sepsis & fitness & \(-0.0002\) & \(5.77413\times 10^{-12}\) & \(5.72004\times 10^{-12}\) & Yes \\ LaplacianDP & Sepsis & precision & \(0.030179246\) & \(0.088575408\) & \(0.158436544\) & No \\ LaplacianDP & Sepsis & generalization & \(0.025346526\) & \(0.001302219\) & \(0.003881089\) & Yes \\ LaplacianDP & BPIC13 & fitness & \(0\) & \(0\) & \(0\) & Yes \\ LaplacianDP & BPIC13 & precision & \(0.058969179\) & \(0.001728048\) & \(0.019910174\) & Yes \\ LaplacianDP & BPIC13 & generalization & \(0.098467829\) & \(3.58955\times 10^{-7}\) & \(6.91213\times 10^{-5}\) & Yes \\ LaplacianDP & CoSeLog & fitness & \(-0.001872186\) & \(3.04058\times 10^{-9}\) & \(2.78417\times 10^{-9}\) & Yes \\ LaplacianDP & CoSeLog & precision & \(0.109447401\) & \(0.036851638\) & \(0.314681253\) & No \\ LaplacianDP & CoSeLog & generalization & \(0.085804276\) & \(0.000600765\) & \(0.027017717\) & Yes \\ SaCoFa & Sepsis & fitness & \(0\) & \(0\) & \(0\) & Yes \\ SaCoFa & Sepsis & precision & \(0.130779246\) & \(0.003907639\) & \(0.277264149\) & No \\ SaCoFa & Sepsis & generalization & \(-0.046453474\) & \(0.001468294\) & \(0.00172437\) & Yes \\ SaCoFa & BPIC13 & fitness & \(0\) & \(0\) & Yes \\ SaCoFa & BPIC13 & precision & \(0.085369179\) & \(0.005421772\) & \(0.105175586\) & No \\ SaCoFa & BPIC13 & generalization & \(0.089067829\) & \(1.29238\times 10^{-7}\) & \(1.33975\times 10^{-5}\) & Yes \\ SaCoFa & CoSeLog & fitness & \(0.000127814\) & \(0\) & \(0\) & Yes \\ SaCoFa & CoSeLog & precision & \(0.0282247401\) & \(0\) & \(0.9958049149\) & No \\ SaCoFa & CoSeLog & generalization & \(0.027604276\) & \(0.00109883364\) & \(0.003638782\) & Yes \\ TraVaS & Sepsis & fitness & \(-9.33627\times 10^{-5}\) & \(2.39473\times 10^{-15}\) & \(2.38423\times 10^{-15}\) & Yes \\ TraVaS & Sepsis & precision & \(0.0106477\) & \(0.000155886\) & \(0.000253978\) & Yes \\ TraVaS & Sepsis & generalization & \(-0.321483243\) & \(0.999921526\) & \(7.30525\times 10^{-7}\) & No \\ TraVaS & BPIC13 & fitness & \(0\) & \(0\) & \(0\) & Yes \\ TraVaS & BPIC13 & precision & \(0.01779995\) & \(9.9117\times 10^{-6}\) & \(2.28312\times 10^{-5}\) & Yes \\ TraVaS & BPIC13 & generalization & \(-0.028381629\) & \(3.51169\times 10^{-5}\) & \(9.22262\times 10^{-6}\) & Yes \\ TraVaS & CoSeLog & fitness & \(-7.94264\times 10^{-5}\) & \(4.61889\times 10^{-13}\) & \(4.60166\times 10^{-13}\) & Yes \\ TraVaS & CoSeLog & precision & \(0.25697802\) & \(7.98887\times 10^{-5}\) & \(0.975872353\) & No \\ TraVaS & CoSeLog & generalization & \(-0.329367836\) & \(0.995901805\) & \(5.36057\times 10^{-5}\) & No \\ TraVaS & Road Traffic Fines & fitness & \(-3.57533\times 10^{-5}\) & \(1.55276\times 10^{-17}\) & \(1.55015\times 10^{-17}\) & Yes \\ TraVaS & Road Traffic Fines & precision & \(-0.186112023\) & \(0.718024594\) & \(7.79982\times 10^{-5}\) & No \\ TraVaS & Road Traffic Fines & generalization & \(-0.109754938\) & \(0.038889465\) & \(0.000197377\) & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 10: One-sample TOST result quality metrics for different different  
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON]. Discovering block-structured process models from event logs--A constructive approach. In Proceedings of the International Conference on Applications and Theory of Petri Nets and Concurrency, Milan, Italy, 24-28 June 2013; Springer: Berlin/Heidelberg, Germany, 2013. * [PERSON] and [PERSON] (2021) [PERSON]; [PERSON] [PERSON]. A novel token-based replay technique to speed up conformance checking and process enhancement. In _Transactions on Petri Nets and Other Models of Concurrency XV_; Springer: Berlin/Heidelberg, Germany, 2021; pp. 1-26. * HIPAA Privacy Rule (2025) HIPAA Privacy Rule.