# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 3 Multilevel Privacy Assurance Evaluation: Results and Evaluation
### At Third-Party Data Processing Level
For precision: Conformance checking is performed with the plugin check precision with ETConformance,\(\rightarrow\)ordered representation\(\rightarrow\)for the algorithm, align precision using ILP-based replayer assuming at most 32,767 tokens\(\rightarrow\)afterward, and the default setting for set parameters is selected\(\rightarrow\)quantified the overall precision, backward precision, and balanced precision. For Complexity: Two matrices, namely ECyM and ECaM, are measured. Both of these are measured using the plugin Show Petri Net Metrics 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. Crucial factors to remember while evaluating are as follows:  
* Proximity to higher fitness values (toward fitness value 1) is essential for higher data utility evaluation, whereas, for a second indicator, any remaining quality indicators add up to the process evaluation. * Only precision and fitness automatically evaluate the suitability of the deduced Petri Net in comparison to the original event-log in ProM. Figure 9: Data analytical approach for third party privacy assurance evaluation. * Complexity is still measured because it quantifies Petri Nets' metrics irrespective of the event log. For example, suppose the values of the Extended Cyclomatic Metric (ECyM) and Extended Cardoso Metric (ECaM) are similar to the test models. In that case, it suggests that the noise-adding plugins are showing unaffected models (without noise). Unaffectedness is further confirmed if precision and fitness values of TMs also coincide with that of the test model. For this reason, the visual comparison between the models is substituted by calculating the respective complexity of the deduced models, i.e., the test models and TMs. Experiments Results and Evaluation  
This section comprises ProM experiments, expectations evaluation, and result discussion. ProM Experiments Results: The quality indicators: fitness, precision, and complexity were quantified for the 2 test models and 12 Training Models (TMs). Two distinctive subgroups of noise-adding plugins and their respective training models were attained. One subgroup showed similar or higher fitness and precision values and changed complexity values from that of the test models named as TMs with high data utility and TMs with low data utility' subgroups showed lower or similar indicators' values. The blue-colored TMs with high data utility subgroup comprises the TMs 2, 4, and 6 in Sepsis event log using ProM 6.9 and TMs: 6, 8, 9, 10, and 11 for hospital billing event log using ProM 6.10. For a detailed description, see Tables 1-3. Twelve noise-adding plugins along with their respective names, type of noise addition, and the capability of better utility/privacy preservation are given in Table 4. Usually, the term 'further anonymization' is used when pseudonymized healthcare datasets are made further privacy preserving by adding noise. Similarly, in this study, the raw Sepsis and Hospital billing event logs were anonymized using noise-adding plugins in ProM 6.9 and ProM 6.10, respectively. The purpose of using different event logs in two different ProM versions was to evaluate the noise of adding plugins in different ProM versions using different healthcare event logs. To verify the findings, two similar plugins were also evaluated in different ProM versions with different event logs where both plugins (i.e., 1 and 6; see Tables 1-3) were judged based on the maintenance of their respective utility levels. The aim is to measure the difference between the data-utility of further anonymized training models compared to that of the original test model in two different versions of ProM.