# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 5 Experiments
### Experimental Results  
Runtime performance experiments were carried out for the Reduced Graph Generation and the Points of Interest Extraction Algorithm 5, and the results, as illustrated in Figure 8, show significant variations among participants. These disparities in execution times may have arisen from differences in the underlying data or system performance. For our experimentation, we used a threshold of \(-0.75\). The figure highlights that some usersexperienced considerably longer runtimes, indicating that their datasets or processes might have involved more complex or larger inputs, leading to extended execution times. A closer examination of the graph reveals a wide range of runtimes, with the shortest being nearly zero and the longest approaching 20 s. This variation could be attributed to differences in graph sizes or the complexity of the Bayesian inference process for each user. Additionally, the more POIs the algorithm detects, the more time it takes to build and search for POI connections in the reduced graph, which explains the longer execution times. As illustrated in Figure 9, the precision for the threshold of \(-0.5\) varied between 0.980 and 0.990, indicating high precision but with some variability among users. This variability can be attributed to differences in the quality of data among users. For example, some users may have had cleaner and better-labeled data, while others may have had noisier or poorly categorized data. By increasing the threshold to \(-0.75\), the precision slightly decreased, ranging from 0.910 to 0.940. This could indicate that the model starts to trade-off between precision and recall, capturing more points of interest but with a slight decrease in precision. For the threshold of \(-1\), the precision further decreased, fluctuating between 0.780 and 0.840. This drop in precision may have been due to an increase in false positives, where the model incorrectly identifies points of interest because of the reduced detection threshold. Figures 9-11 show, respectively, the precision, recall, and F1 POI scores for three different thresholds \(-0.5\), \(-0.75\), and \(-1\) across multiple users. Figure 10 shows that recall is low for the \(-0.5\) threshold, with values between 0.42 and 0.50. This poor recall performance suggests that the model failed to detect a large proportion of the points of interest. By increasing the threshold to \(-0.75\), the recall improved significantly, ranging from 0.70 to 0.78. This improvement indicates that the model became  
Figure 8: Runtime execution for reduced graph creation. Figure 9: Precision scores obtained. more sensitive to the detection of points of interest, thus capturing a greater proportion of true points of interest. For the \(-1\) threshold, the recall continued to show improvement, with values ranging from 0.80 to 0.88, although there is still considerable variability among users. As illustrated in Figure 11, the F1 POI score for the threshold of \(-0.5\) varied significantly between 0.58 and 0.66, reflecting unstable model performance. It shows that, despite high precision, the low recall value pulled the F1 POI score down. By increasing the threshold to \(-0.75\), the F1 POI score improved, with values ranging from 0.78 to 0.85, indicating better overall performance. This improvement in the F1 POI score indicates a better balance between precision and recall, meaning that the model was more successful in correctly identifying points of interest while reducing detection errors. For the threshold of \(-1\), the F1 POI score is similar to that of the \(-0.75\) threshold, ranging between 0.80 and 0.85, but it still showed significant fluctuations. The results suggest that the \(-0.75\) threshold offers a good compromise between precision and recall, providing a high F1 POI score and better stability than the other thresholds. However, there is notable variability in performance between users. This variability is attributed to the quality of the data used to train and test the model.