# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 3 Multilevel Privacy Assurance Evaluation: Results and Evaluation
### At Third-Party Data Processing Level  
For third-party data processing, we focused on the PM domain using real-world healthcare event logs. Third Party Data Processing in PM domain using publicly available PM tools and techniques on real-world Healthcare Datasets: This research verifies the level of privacy preservation of noise-adding plugins in the publicly available Process Mining tool ProM. The privacy preservation is evaluated along with their respective data utility. The data utility of the plugins is verified using quality indicators' of the deduced (noise-added) business process models called Petri Nets. Furthermore, the noise-added Petri Nets are compared against the original Petri Nets for assessing the increase/decrease: in both the privacy and data utility of the event logs. Process Mining and Data Analytical Approach: For an overview, an illustration of our data-analytics approach for third party privacy assurance evaluation is given in Figure 9. The original event logs were made more privacy preserving [82] by using 12 noise-adding plugins in two versions of ProM (6.9-6.10). For both the event logs and in both the ProM versions, two similar (plugins producing TM1 and TM6) and ten different (five plugins each) noise-adding plugins were evaluated based on respective quality indicators (explained below) of the noise-added Petri Nets, i.e., Training Models (TMs). For each test model of the original event log, the inductive miner plugin was used to deduce the test model's Petri Net in both versions of the ProM tool. An inductive miner was specially used to maintain the soundness of the event logs [83], also because the inductive miner nicely evaluated the event logs and gave good performing Petri Nets with higher utility indicators' values, i.e., fitness, precision, simplicity/complexity, and generalization [83]. Conformance checking (utility) indicators, i.e., fitness, precision, and complexity, were  
Figure 8: Financial gain/loss of healthcare enterprises and healthcare metadata utility. evaluated using ProM with'replay, conformance checking, and metrics analysis' plugins. This allowed us to skip manual (visual) evaluations. Fitness (domain: 0...1) quantifies the level of observed behavior in the derived model compared to the event-log from 0 to 1. Good fitness indicates a convenient replay of the behavior seen in the event log [47; 48; 53; 84]. Fitness is computed based on all three fitness matrices: move-model fitness, move-log fitness, and move-trace fitness. Precision (domain: 0...1) computes that segment of the model's behavior that is not visible in the event-log from 0-1. Precision is computed based on all three precision matrices, i.e., overall precision along with backward precision and balanced precision [84]. Complexity itself is an inverse of the quality indicator, simplicity. Simplicity is quantified by its inverse, i.e., complexity. Complexity is measured using the Extended Cyclomatic Metric, i.e., ECaM of Petri Nets, independent of the event log. The higher the value of these matrices, the lower the simplicity level of the respective model. A higher complexity level implies more errors [53]. The step-wise procedure to quantify the quality indicators for the real-world test models is as given below:  
For fitness: In ProM, the Sepsis/hospital billing event-log is imported\(\rightarrow\)deduce the test model, using, Mine a Petri Net using Inductive Miner\(\rightarrow\)the Petri Net is replayed along the original Sepsis/hospital billing event-log using Replay a log on Petri net for conformance checking\(\rightarrow\)concept name, and ILP based replayer assuming 32,767 tokens are opted\(\rightarrow\)for the rest, the default setting is kept\(\rightarrow\)in visualization, the PNET alignments setting\(\rightarrow\)in global statistics the move-model fitness, move-log fitness, and trace-fitness are measured\(\rightarrow\)visualizing, project alignment to log, gave log's each trace alignment with the model.