## 2 Related Work
The practical effectiveness of PRUDEnce is shown with real mobility data, exploring presence, trajectory, and road segment data formats. Our decision to utilize PRUDEnce was based on its flexible extension and suitability for trajectory data. The computational intensity of PRUDEnce has prompted exploration into machine learning approaches aimed at predicting privacy risk, thereby bypassing the need for computationally exhaustive processes. [PERSON] et al. (2017) [9] present a swift and adaptable method for estimating privacy risk in human mobility data. Their approach involves training classifiers to link individual mobility patterns with different privacy risk levels. Another important advancement in this field is the EXPERT framework, developed by [PERSON] et al. (2020) [10]. This framework refines PRUDEnce by introducing a machine learning methodology that proficiently forecasts privacy risk from sequential data. Moreover, the framework enhances the interpretability of these predictions by incorporating methodologies such as SHAP [15] and LIME [16]. Another study proposed by [PERSON] et al. (2023) [17] presents an optimization of EXPERT, the EXPHLOT. Authors use distinct time series classifications, such as ROCKET and INCEPTIONTIME, to improve risk prediction while reducing computation time. While previous works focus on improving efficiency and reducing computational demands for privacy risk assessment, these works often require an initial conventional risk analysis to generate training data for the risk-predicting machine learning model. Our proposal offers an approach that aims to enhance the computational risk algorithm. By directly optimizing the risk assessment process, our methodology eliminates unnecessary computation. This streamlined approach not only reduces the computational time but also simplifies the overall risk assessment pipeline. Our strategy for evaluating the maximum risk and reducing the computation time is to select low-entropy trajectory features to target high-risk data and reduce the data used to evaluate the risk. [PERSON] et al. (2017) [9] showed that entropy has an important impact on predicting features/locations in machine learning models. The idea is that location entropy is related to uniqueness, which is the main measure of anonymity. If a user passes through high-entropy locations, where, therefore, many different people pass through, the uniqueness of their mobility profile is lost as the general movement blurs it. In EXPHLOT [17], authors show that they have the highest entropy locations, evaluating only the lowest entropy locations. In this way, they focus on locations with fewer individuals visiting, focusing on explaining high-risk predictions. In our work, we reduced the computation time for maximum risk evaluation since it would yield the highest risk values. We also went a step beyond checking not only location entropy but also time entropy. Using different attacks and adversary knowledge sizes, we used the \(p\)-value and Kolmogorov-Smirnov test [18; 19] to prove the efficiency of using low-entropy values to reduce maximum risk computation. Our proposal introduces novel techniques to more efficiently identify and prioritize high-risk trajectories. By leveraging insights from trajectory data characteristics, such as inherent uniqueness and temporal dependencies, our algorithm can highlight trajectories with elevated privacy risks.