# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 2 Motivating Scenario, Challenges, and Objectives
### Running Example  
Let us consider RideEase, an application developed by the company BeeTeam to streamline vehicle sharing among its employees. Environmentally conscious and committed to ecological transition, this initiative offers reward points to encourage the use of sustainable transport solutions, thus motivating employees to adopt eco-friendly carpooling practices. Each year, employees who earn the most points are rewarded. [PERSON] and [PERSON], two BeeTeam employees, actively use the application to increase their chances of receiving a reward at the end of the year. [PERSON] frequently contributes as a driver, offering rides that coincide with his work schedule and preferred routes. [PERSON], on the other hand, uses the application to find rides that match her specific needs and schedule. Figure 1 describes how this application works. To effectively recommend routes, [PERSON] must continuously share his location with the application, while [PERSON] only does so when she wants to make a trip. The displayed map (on the right) shows [PERSON]'s usual routes (blue markers) and [PERSON]'s preferred destinations (red markers). The arrows indicate the regular routes taken by [PERSON], while the dotted lines represent [PERSON]'s potential routes.  
However, the use of RideEase raises privacy concerns. For example, if [PERSON] offers rides from his home every morning, this could reveal his home address to regular passengers. Additionally, the history of shared routes could show precise movement patterns, allowing deductions about not only his home but also other frequently visited places (like a gym or a specific supermarket). Similarly, if [PERSON] always chooses routes that drop her off near a  
Figure 1: Motivating scenario.  
clinic she visits regularly, it could inadvertently disclose sensitive information about her private life (such as health details). Repeated use of the application for similar routes might lead other users or even the application's administrators (who have access to the logs) to notice specific habits, leading to privacy breaches. Assume that [PERSON], another employee, secretly exploits RideEase to track his colleagues' whereabouts, including [PERSON] and [PERSON], without their consent. By exploiting the system vulnerabilities, [PERSON] can clandestinely monitor their movements and frequent stops, uncovering many details about their daily routines and compromising their privacy. For example, he could deduce [PERSON]'s precise work hours, his personal activities after work, or [PERSON]'s regular medical visits, thereby severely compromising their security and confidentiality. Here are some examples of the privacy issues that could arise from [PERSON]'s malicious use of RideEase:  
* Blackmail: By discovering sensitive information about their movements, such as visits to health clinics, [PERSON] could threaten to disclose this information to extort money or other favors from them.
* Stalking or Harassment: [PERSON] can analyze the frequency and timing of [PERSON]'s and [PERSON]'s visits to certain locations to predict their future movements, allowing for physical stalking or digital harassment.
* Commercial Exploitation: [PERSON] could sell [PERSON]'s and [PERSON]'s location data to interested companies or individuals, further compromising their privacy and exposing them to unwanted solicitations (e.g., targeted advertising).  
However, such semi-reliable nature of RideEase introduces significant privacy concerns.