# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 3 Multilevel Privacy Assurance Evaluation: Results and Evaluation
### Twelve Noise-Adding Plugins and Their Privacy Utility Tradeoff  
Expectations Evaluation including Discussion: The TMs with high data utility' results verify the aforementioned underlying reasons (see research expectation in Section 3.3). The blue-colored TMs (see Tables 1-3) give similar or better fitness and precision values and different complexity values to that of the test model. The TMs with lower data utility show either lower fitness values (the essential first step in data quality assurance) or lower precision values [84] or similar fitness, precision, and unaffected complexity. Lower complexity shows the higher simplicity and simplified Petri Net, but it is less of a concern here. However, the differing complexity values are important in diagnosing that the deduced process models added noise and were not identical to the test models. The blue-colored TMs results in Tables 1-3 verify our research expectation, \"evaluating PUT of noise-adding plugins in ProM. The **research expectation** is: in evaluating PUT of noise-adding plugins in ProM, if a training model shows similar or higher fitness and precision with changed complexity values, then the training model shows higher data utility.\" However, does this evaluation imply that the respective plugins will always give higher data utility for each event log? The performance of plugins highly depends upon the quality and domain of the event log. An event log with (comparatively) less sensitive and nicely put data would allow the plugin to give better data utility (Hospital billing is comparatively lesser sensitive and gives better data utility see TM1 and TM6). This was further confirmed by evaluating another publicly available (healthcare) event log (i.e., Urineweginfectie (UWI-casus) logbook) [65]. The event log gave around 0.1 precision and 0.6 fitness for its test model using similar plugins with Prom 6.10. Still, the TMs with higher data utility would give similar or higher fitness and precision values (i.e., 0.1 precision and \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Noise-Adding Plugin** & **Plugin’s Name** & \
\begin{tabular}{c} **Type of Noise** \\ **Addition** \\ \end{tabular} & **Higher Utility** & **Higher Privacy** \\ \hline \multirow{4}{*}{5 th} & Add chaotic activity to log, infrequent activity to log, infrequent activity to log, positioned activities & \(\times\) & ✓ \\ \hline \multirow{4}{*}{6 th} & Low-frequency-filtered log & Outliers removal & ✓ & \(\times\) \\ \hline \multirow{4}{*}{7 th} & High-frequency-filtered log & Filtering out high-frequency activities & \(\times\) & ✓ \\ \hline \multirow{2}{*}{8 th} & Anonymized event log & Obscuring of event attributes & ✓ & \(\times\) \\ \hline \multirow{4}{*}{9 th} & Obfuscate literal event attribute values & Obscuring of event attributes & ✓ & \(\times\) \\ \hline \multirow{4}{*}{10 th} & Obfuscate event attribute names timestamps & Obscuring of event attributes’ names (trace changing) & & \\ \hline \multirow{4}{*}{11 th} & Add/change concept: name of event from classifier &
\begin{tabular}{c} Addition/changing of events’s names \\ \end{tabular} & ✓ & \(\times\) \\ \hline \multirow{2}{*}{12 th} & Reversed log & Reversing of trace & \(\times\) & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._0.6 fitness) and vice versa. Thus, the respective utility indicators' values vary but not the data utility of the plugins. Threats to Validity: Plugins' performance depends directly upon the quality and domain of the event log. Plugins give better data utility to the more suitable event logs, especially when trained algorithms within the plugins nicely find integrated information from the event log and efficiently perform their tasks. Domains with less sensitive event logs allow the plugins to give even better data utility. Still, the plugins with higher data utility maintain better data utility on the less suitable (lower quality) event log and vice versa. The conclusion of the research work relies upon the correct usage of the mentioned plugins.