# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 1 Introduction  
The rise of mobile Internet has radically changed the way we access services, ideas, and content. Companies and institutions that once relied exclusively on their employees or specific suppliers to accomplish tasks are now opening up to a wide range of talent worldwide thanks to the magic of crowdsourcing. [PERSON] first used the term \"crowdsourcing\" to describe this trend in Wired Magazine in June 2006, highlighting the idea of outsourcing tasks to a large network of people via an open call [1]. In the field of crowdsourcing, one branch stands out: spatial crowdsourcing. This is a practice in which individuals are asked to perform tasks relating to certain places, which frequently involve sharing their physical location. This method can range from the simple transmission of geolocated data, such as real-time traffic reporting or air quality assessment, to more complicated duties that need on-site presence, such as parcel delivery. Uber, for example, uses spatial crowdsourcing to connect drivers and passengers, demonstrating the efficacy of this approach. However, with the rapid expansion of crowdsourcing, major concerns are emerging about the security and confidentiality of personal data, especially with regard to the location of participants. Crowdsourcing platforms need usually to know the precise geographic location of participants to properly assign tasks, causing a dilemma between the need to share location data and the risks associated with such disclosure. A variety of techniques have been proposed to mitigate the dangers connected with the confidentiality of locationdata in crowdsourcing. These methods include the use of dummy locations [2; 3], spatial cloaking [4], differential privacy [5], k-anonymity [5; 6], and encryption [7; 8; 9]. However, these techniques fail to adequately assess the risk caused by the constant disclosure of participants' location information to crowdsourcing service providers. Furthermore, a critical but often overlooked feature of location security is vulnerability to inference attacks [10; 11], which can infer sensitive information from shared data. Malicious attackers can retrieve sensitive information from location data, even if it is anonymized or masked. Analysis of travel patterns can reveal deeply private personal information, such as individual's habits, dates, or frequently visited locations, including home and workplace [12]. German politician [PERSON] used Deutsche Telekom to release six months of his phone's location data, later publishing it as an interactive map [13]. This case vividly illustrated how combining location data with contextual information could lead to severe privacy breaches. Similarly, a Wall Street Journal report revealed that tech giants like Apple and Google collect vast amounts of location data to develop location-based services, sparking widespread privacy concerns [14]. To safeguard individual privacy, worldwide regulatory initiatives have been launched, including in Europe, the USA, Japan, Australia, and the Asia-Pacific Economic Cooperation. These aim to bolster privacy via bodies like France's CNIL, proposing standards such as ISO/IEC 29100 for IT security and privacy and AICPA/CICA for personal information risk evaluation [15; 16; 17; 18]. Despite these efforts, regulations alone can't ensure privacy without data sanitation. Additionally, accurately measuring privacy risks remains a significant challenge. Specifically, when exploring the construction of a user mobility model to bolster privacy protections in crowdsourcing ecosystems, a set of fundamental but challenging issues can be encountered to delve deeper into the realm of data privacy and user behavior:  
1. How can one model user mobility effectively to prevent inference attacks? 2. How does one accurately pinpoint Points of interest (POIs) to reflect an individual's movement within a user mobility model? 3.