# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 3 Multilevel Privacy Assurance Evaluation: Results and Evaluation
### Twelve Noise-Adding Plugins and Their Privacy Utility Tradeoff
Thus, the indicators; fitness, precision, and complexity are evaluated by noise-added Petri Nets and respective noise-adding plugins using the ProM tool. The step-by-step procedures to evaluate fitness, precision, and complexity are given in Section 3.3. For fitness: move-log, move-model, and trace fitness, for precision: precision, backward precision, balanced precision, and for complexity: ECyM and ECaM matrices are evaluated, respectively. Fitness and precision values are measured using ProM because they calculate the comparative values between the deduced Petri Nets and the original event logs. Moreover, the varied values of complexity matrices exhibit the noise addition to the originally deduced test Petri Net. Using ProM, we found that the Petri Nets with higher data utility have similar or higher fitness and precision values with changed complexity values. Based on our findings, we further conclude that well-structured event logs give higher utility values. Nevertheless, this does not hamper or affect the performance of noise-adding plugins. We evaluated 12 noise-adding plugins of ProM using real-world Sepsis and Hospital billing datasets. The ProM experiments gave us two subgroups of plugins based on similarity/dissimilarity between respective Training Models (TMs)--namely, TMs with high data utility displaying somewhat similar or higher fitness and precision values to the original test model but changed complexity values. The TMs with low data utility exhibited lower fitness and precision values than the respective test models or with similar (complexity) indicators' values. Plugins add chaotic events to log and add an infrequent chaotic event to log, and low frequency filtered out showed high data utility compared to the test model of Sepsis event log using ProM 6.9. For the Hospital billing event log, ProM 6.10 revealed that the: anonymized event log, obfuscate literal event attribute values, obfuscate event attribute name, add change concept, and low frequency filtered out gave similar or higher data utility. The findings are based on similar or higher fitness and precision values and different complexity values from that of the respective test model. TM\({}_{6}\) was produced using: low-frequency filtered-out plugin and was used for both the event logs and gave maximum data utility in both the logs with ProM 6.9 and 6.10, respectively. Moreover, the TM\({}_{1}\), add noise to log filter, was also produced with a similar plugin that was evaluated on both different logs using two different versions of ProM (i.e., 6.9 and 6.10) and gave a similar result of lower data utility than respective test models. The black-colored noise-adding, privacy-preserving plugins badly affected data utility with increased privacy preservation. Table 5 provides a concise description of the empirical and normative evaluation of all three levels of multilevel privacy assurance. The research provides the multilevel privacy assurance evaluation of fundamental privacy-preserving measures for sensitive healthcare metadata handling in the Dutch healthcare landscape. Table 5 provides the crux of the research work with the usage of respective techniques for each level ranging from individual to inter-/intra-organizational to the third-party data processor level. As Table 5 shows that for the first level, we provided the normative evaluation and its illustration with e\({}^{3}\)-value modeling, which was later verified with expert opinion. At this level, the empirical evaluation was not performed because the Informed Consent in its essence (for basic aspects see Figure 11) deteriorates with each level from individual to inter-/intra-organizational to third-party data processing level. For the second inter-/intra-organizational level, we provided both normative and empirical evaluation and illustrated the findings using e3-value modeling and REA ontology, respectively. Both evaluations were verified by (in-field) expert opinion.