# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 3 Related Work
### Poi Extraction Techniques
[30] extracted POIs through analyzing temporal patterns of arrivals and durations at locations using GPS and WiFi logs, offering insights into movement patterns and significant locations. Table 2 provides a comparative analysis of various POI extraction approaches based on four key criteria: parameter sensitivity, noise sensitivity, temporal granularity dependency, and semantic consideration. * Parameter Sensitivity: This criterion evaluates how changes in parameters, such as distance thresholds or the minimum number of points in a cluster, affect the extraction results. The sensitivity levels are categorized as follows: high sensitivity indicates that minor parameter adjustments significantly affect the outcomes, medium sensitivity means that changes in parameters lead to moderate variations in the results, and low sensitivity shows that the results are stable and exhibit little variation despite changes in parameters. * Noise Sensitivity: This criterion evaluates the method's robustness in handling noisy data, including outliers or irrelevant data points. Sensitivity levels are defined as follows: high sensitivity means that the presence of noise substantially degrades the accuracy of the extraction, medium sensitivity indicates that noise affects the results but the impact is generally manageable, and low sensitivity shows that the method retains accuracy even with significant levels of noise. * Temporal Granularity Dependency: This criterion assesses whether the effectiveness of the method is influenced by the temporal resolution of the data. It is analyzed as follows: if the accuracy of POI extraction improves with finer temporal granularity, the answer is yes; if the method delivers consistent performance regardless of the temporal resolution, the answer is no. Semantic Consideration: This criterion indicates whether the method utilizes semantic analysis to understand the context or significance of locations. Semantic consideration can be evaluated as follows: if semantic analysis is integral and enhances the contextual relevance of the extracted POIs, the answer is yes; if the method relies solely on geographical and temporal data without incorporating semantic interpretations, the answer is no. Approaches employing clustering, exemplified by [PERSON] et al. [24; 31], [PERSON] et al. [25], and others, generally exhibit high parameter sensitivity and moderate noise sensitivity, with minimal dependency on temporal granularity and lacking in semantic consideration. Conversely, heuristic-based methods, as seen with [PERSON] et al. [11], show varied sensitivity to parameters and noise, but they are distinctive in their occasional integration of semantic analysis. Our approach distinguishes itself by demonstrating low sensitivity to parameters, enabling stable and robust POI extraction. It uniquely benefits from fine temporal granularity and incorporates comprehensive semantic analysis, which significantly enhances the contextual relevance and accuracy of the extracted POIs. This makes it an advanced solution within the spectrum of existing methodologies, offering substantial improvements in both the precision and utility of POI extraction for user privacy.