# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 5 Experiments
### Setting  
#### 5.1.1 Dataset Collection and Analysis  
We have developed a cross-platform mobile application designed to collect GPS data from 10 volunteers who were willing to share their locations. Prior to data collection,each participant was provided with a detailed consent form outlining the types of data to be collected, the purpose of the research, and the steps taken to ensure that all collected data would be anonymized. The data were collected every 60 s, enabling us to monitor the movements and dwell times of participants across various locations over a period of 3 months. The total distance covered by these GPS logs exceeded 10,000 km. The collected data include timestamps and GPS coordinates, which provide essential information for constructing and evaluating the user mobility graph and the POI extraction algorithm.  
All participants filled out and signed a consent form via [[https://form.jotform.com/242243188677566](https://form.jotform.com/242243188677566)]([https://form.jotform.com/242243188677566](https://form.jotform.com/242243188677566)) (accessed on 1 March 2024) prior to the start of the study. This ensured that we had their official consent and provided them with detailed information on how their data would be used for the study. To ensure privacy, each participant was assigned a unique identifier via that was not linked to their personal information, making it impossible to trace the data back to an individual. Recognizing the inherent risk of re-identification with GPS data, we anonymized the information by rounding timestamps and obscuring specific location details that could potentially reveal a participant's identity. Additionally, all data were encrypted to safeguard them from unauthorized access, ensuring that even if intercepted, they remained secure. Access to the raw data was strictly limited to authorized members of our research team, who were the only individuals permitted to handle the information.  
Figure 6 displays a screenshot of the mobile application utilized for GPS data collection, while Figure 7 illustrates a sample of the distribution of collected GPS data points plotted on a map (The points on the map are too dense but do not represent paths). Table 5 displays the fundamental statistics of our dataset. The numbers from 01 to 10 in the first column represent the user IDs. The second column indicates the number of tuples collected. Column 3 details the categories of significant locations, while Column 4 lists the real POIs suggested by users. The final column shows the total distance of the collected data.  
The occurrence of positioning errors in GPS loggers is a well-known issue, particularly in indoor environments [32]. [PERSON] et al. [33] reported that even state-of-the-art GPS loggers experience positioning errors caused by environmental factors. Their findings showed that fewer than 80% of the track points in their experiments were within 20 m of the true locations. Our preliminary experiment revealed that similar errors occurred for track points obtained outside buildings. GPS positioning errors seem inevitable.