## 2 Related Work  
Quantitative privacy risk assessment for mobility trajectory and other types of data is a well-studied topic. [PERSON] et al. 's (2009) [4] approach involves recommending secure configurations through a smart bootstrapping system, aiming to enhance understanding and management of the risks associated with non-controlled data disclosure. The authors utilized a probability-based approach, demonstrating that it is possible to reduce computation time by leveraging previously calculated risk values to predict future risk values. [PERSON] et al. (2014) [6] propose a modification-based anonymization approach and evaluate privacy risk based on the uniqueness of trajectory data. The authors employed a probability of re-identification based on sub-trajectories and demonstrated that, by reducing the overall trajectory size--specifically, by removing the highest-risk sub-trajectories--the re-identification risk is significantly decreased. In [PERSON] et al. (2015) [5], their research investigates the privacy implications of the list of apps installed by users on smartphones, emphasizing the re-identifiability issue. Analyzing a dataset with 54,893 Android users over 7 months, the study finds that merely four installed apps are sufficient for user re-identification over 95% of the time. Remarkably, the complete list of installed apps is unique for 99% of users, making it susceptible to tracking or profiling by services like Twitter with access to this information. In [12], their proposed framework integrates run-time risk assessment into information disclosure access control, utilizing disclosure risk for decision-making. Access-control decisions are driven by the associated disclosure risk of data access requests, and adaptive anonymization serves as a method for mitigating risks, ensuring privacy preservation. Other studies in the literature explore re-identification risk as a privacy measure within the realms of network and social media data [7; 8]. In [7], the authors introduce a framework for assessing privacy and anonymity within social networks and introduce a new re-identification algorithm aimed at anonymized social network graphs. To demonstrate its effectiveness on real-world networks, they showed that a third of users with accounts on both Twitter and Flickr can be re-identified in the anonymized Twitter graph with a 12% error rate. Finally, ref. [8] showed that, based on social media behavior, it is possible to re-identify passive web visits to the host. Their method combines a public follower graph on social media with posting behaviors and time-based inferences and proved to be efficient in re-identifying the users. [PERSON] et al. (2021) [13] propose EDEN, selecting optimal Location Privacy Protection Mechanisms using federated learning without exposing raw traces, demonstrating superior privacy vs. utility tradeoff across real-world datasets. [PERSON] et al. (2022) [14] introduce the Personal Data Analyser, which employs automated data monitoring with Regular Expressions, NLP, and machine learning to enhance privacy. Integrated into the PoSeID-on platform, it alerts users to risks with crisp and fuzzy models validated through real-world use cases. In this work, we adopted the PRUDEnce framework introduced by [PERSON] et al., as elucidated in their seminal work [11] and previously presented in Section 6. PRUDEnce was introduced as a system that deals with finding a balance between privacy risk and data usefulness when sharing sensitive human activity data. This framework offers a methodology for the computation of privacy risk in a data-driven fashion. At its essence, PRUDEnce revolves around the foundational principle of k-anonymity, wherein the privacy risk assessment is linked to the dimensions of k-sets associated with each individual in the dataset. The method checks out real privacy risks for users and ensures data quality for those not at risk. Data providers can try different changes to strike the right balance between privacy and usefulness.