# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 5 Experiments
### Experimental Results
It is crucial to ensure that the identified locations are accurate and that the categories are correctly identified for the Bayesian network. Incorrect classification or inaccurate location data can greatly affect the model's ability to infer correctly. The categories of points of interest must be consistent and correctly labeled to avoid confusion when constructing the Conditional Probability Tables. Additionally, the model identified points of interest with high precision, which is important for privacy due to the high probability calculated with the utility function. We conducted experiments using the DJ cluster algorithm, testing it across a range of parameters to evaluate its performance. The parameters we varied include Eps (km) with values {0.01, 0.02, 0.05, 0.1, 0.2}, Merge distance (km) with values {0.02, 0.04, 0.1, 0.2, 0.4}, and Time shift (hours) with values {1, 2, 3, 4, 5, 6}. The goal was to observe how changes  
Figure 11: F1 POI scores obtained. Figure 10: Recall scores obtained. in these parameters would affect the algorithm's ability to accurately identify POIs across multiple users. The results, as depicted in the provided Figure 12, reveal that the DJ cluster algorithm demonstrated a moderate and inconsistent performance. When analyzing the precision across different users, we observed a range between 0.46 and 0.54. This variation indicates that while the algorithm can correctly identify true positives to some extent, its accuracy is inconsistent, leading to fluctuations in performance depending on the user data being analyzed. This inconsistency could be attributed to differences in the quality and structure of data across users, which may affect the algorithm's ability to accurately cluster POI. The recall values, which measure the algorithm's ability to capture all relevant POIs, are slightly more stable, ranging from 0.55 to 0.61. However, these values are still moderate, suggesting that the algorithm was missing a significant number of true positives. Despite the relative stability in recall, the moderate performance indicates that the DJ cluster algorithm may not be adequately sensitive to detect all relevant points of interest, which is critical in applications requiring comprehensive detection. The F-measure, which balances both precision and recall, fluctuated between 0.50 and 0.57 across the users. This fluctuation further underscores the variability in the algorithm's performance. The F-measure is particularly important, as it reflects the overall effectiveness of the algorithm by considering both its precision and recall. The observed fluctuations suggest that while the algorithm might perform well in certain instances, it lacks consistency, which could undermine its reliability in practical applications. Overall, the performance of the DJ cluster algorithm, as illustrated in these graphs, indicates that while it can provide moderate results, its inconsistency across different users raises concerns about its robustness and generalizability. The significant variations in the precision, recall, and F-measure suggest that the algorithm may struggle with varying data qualities and complexities, leading to unreliable identification of points of interest in certain cases.