# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 3 Related Work
### Poi Extraction Techniques  
The process of pinpointing POIs constitutes a type of inferential attack, leveraging either a heuristic approach or a clustering technique to detect locations that reflect individual's interests. These POIs could include, for example, someone's home, workplace, or frequented spots like a gym, cinema, or a political party's main office. Disclosing an \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & & & \\ \hline \hline \multirow{2}{*}{**Related Work**} & \multirow{2}{*}{**Relationships**} & **Spatial and Temporal** & **Scalability and** & **Real-Time Data** \\  & & **Dynamics** & **Flexibility** & **Handling** \\ \hline [PERSON] et al. [19] & Low & Yes & Medium & No \\ [PERSON] et al. [20] & High & Yes & Medium & Yes \\ [PERSON] et al. [21] & Low & No & Low & No \\ [PERSON] et al. [22] & High & Yes & High & No \\ [PERSON] et al. [23] & Low & Yes & No & Yes \\ \hline \hline \multicolumn{5}{c}{Our Graph-Based Approach} \\ \hline Our Approach & High & Yes & High & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of location modeling techniques. individual's specific POIs could potentially compromise their privacy, since such data might be exploited to deduce sensitive information. The vast majority of published work uses threshold-based spatiotemporal clustering methods, where the clusters represent stay segments of the trajectory. In Ref. [24], the authors introduced an algorithm designed to identify significant places from a series of geographic coordinates. These significant places, characterized by clusters of locations where a user spends a considerable amount of time, are not effectively identified by traditional clustering algorithms. The authors highlight three main limitations of conventional approaches: the need for predefined cluster numbers, the inclusion of irrelevant locations in clusters, and the high computational demand. The authors of [25] presented a novel approach for evaluating user similarity based on their location histories. The significant contribution of this study is the development of a methodology that leverages spatial and temporal aspects of users' location data to identify patterns of movement and frequent locations. This method enables the discovery of similarities between users not just based on shared locations but also considering the temporal patterns of their visits. [PERSON] et al. in [26] introduced DeepStay, a transformer-based model trained under weak and self-supervision on location trajectories to predict stay regions. This model, the first of its kind in deep learning evaluated on a public, labeled dataset, outperformed existing methods in extracting stay regions. DeepStay also significantly enhance the detection of transportation modes from GPS trajectories, demonstrating its utility beyond mere POI extraction. [PERSON] et al. introduced in [27] a method for POIs utilizing a spatiotemporal adaptation of DBSCAN, termed DJ cluster. This algorithm adjusts to the specific mobility patterns of the individual being analyzed, differing from k-means where the quantity of clusters must be predetermined. Specifically, the DJ cluster algorithm requires only the maximum radius of clusters and the minimum number of mobility traces within a cluster as input parameters, making it a flexible approach for POI extraction based on individual behavior. In their study, [PERSON] et al. [28] analyzed data from 62 Geolife [29] users to identify common POIs by detecting stops--locations where a user remains beyond a set time and distance threshold--merging these stops into POIs based on distance, time, and frequency criteria. [PERSON] [11] introduced a heuristic based on the likelihood of a user being at home at specific times, suggesting the highest probability occurs from 6 PM to 8 AM, with a median home location error of 61 m in a study of 172 drivers. Similarly, [PERSON] et al.