# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 4 Proposed Approach
### Contribution Insights  
Figure 2 provides an overview of our approach which is able to generate user mobility within the context of crowdsourcing. It contains several phases. The initial phase begins with the collection of raw GPS trajectory data. These data typically consist of sequential location points captured at regular intervals, representing the routes taken by participants. However, raw GPS data often contain a significant level of noise and redundant information  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{3}{c}{**Criteria**} \\ \hline
**Approaches** & **Parameter Sensitivity** & **Noise Sensitivity** & \
\begin{tabular}{c} **Temporal Granularity** \\ **Dependency** \\ \end{tabular} &
\begin{tabular}{c} **Semantic** \\ **Consideration** \\ \end{tabular} \\ \hline [PERSON] et al. [24; 31] & high & medium & no & no \\ \hline [PERSON] et al. [25] & high & medium & yes & no \\ \hline [PERSON] et al. [26] & medium & low & no & no \\ \hline [PERSON] et al. [27] & high & high & no & no \\ \hline [PERSON] et al. [28] & low & medium & no & no \\ \hline [PERSON] et al. [11] & low & medium & yes & yes \\ \hline [PERSON] et al. [30] & low & high & no & no \\ \hline Our Approach & low & medium & yes & yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of POI extraction approaches.  
due to GPS signal inaccuracies and other environmental factors. Therefore, we apply a preprocessing process on the historical location data in order to clean and organize them in several ways.  
In fact, this preprocessing algorithm allows us to reduce noise, extract metadata, eliminate short stays and redundancy, and filter out unimportant locations. This process transforms the raw data into a more usable format for the subsequent stages of analysis. Once the data are preprocessed, the graph generation phase starts. In this phase, as presented in Section 4.4, significant locations are identified and defined as nodes. Then, edges are created to represent the paths between these nodes and annotated with corresponding weights while preserving the temporal sequence of the journeys. The final phase includes the POI extraction algorithm, which is detailed in Section 4.5. This algorithm refines the graph by identifying significant POIs, which are locations of particular interest to the participants. This helps to simplify the graph, highlighting the main movement patterns and key locations. The reduced graph contains actual, detailed information about the user's movements and locations. While this reduction process focuses on relevant data points, it still maintains significant information that could potentially reveal sensitive details about the user. The nodes and edges in the reduced graph represent real POIs and transitions, which can include personal places such as home, work, or frequently visited locations. As a result, the graph retains the potential to expose private aspects of the user's life. In our future work, we will implement measures to protect user privacy within the reduced graph, ensuring that sensitive information remains secure.  
This approach ensures that the generated graph accurately represents the true movement patterns of users. It enhances the efficiency of the analysis by reducing data complexity and provides deeper insights into user mobility, enabling a better understanding of user movement behaviors.  
In the following section, we present several preliminary definitions before detailing each step of our approach.