## 7 Conclusions and Future Work  
Privacy risk assessment is a crucial aspect of any privacy-preserving process, which involves understanding which individuals in the data are vulnerable to privacy violations and quantifying the associated risk. One significant challenge in assessing risk is reducing the computational processing associated with the adversary's background knowledge set size and risk assessment. Most of the current works on privacy focus on Deferentially Private Machine Learning techniques and/or federated learning approaches. In this work, we focus on privacy risk assessment to increase the ability of researchers and practitioners to correctly understand what kinds of risks are inherently present in the data they are using. With this work, we hope to provide concrete solutions to enable efficient privacy risk estimation for human mobility data. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Attack** & **Dataset** & **\%** \\ \hline Location Sequence & Wi-Fi & 3.68 \\ Visit & Wi-Fi & 52.30 \\ Location Sequence & Headcrumbs & 100 \\ Visit & Headcrumbs & 100 \\ Location Sequence & Foursquare & 99.88 \\ Visit & Foursquare & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Trajectories directly assigned with a risk of 1 per attack. The main contribution of this article addresses the challenge posed by the computational complexity of privacy risk evaluation. We focused on potential methodologies to mitigate this complexity, aiming to reduce the combination set and optimize code performance for computing the highest risk trajectories. Leveraging the inherent uniqueness of trajectory data, we aimed to minimize the size of the combination set and simplify the risk evaluation process for trajectories with distinctive attributes. Furthermore, we enhanced computational efficiency by implementing strategies to store essential information in memory, thereby minimizing the need for redundant computations. As a result of the experiments, while the proposed optimization strategies showed promise in enhancing computational efficiency and risk assessment accuracy, their effectiveness varied depending on the uniqueness of features within different datasets. Understanding the nature of features is crucial in selecting appropriate risk assessment approaches and optimizations. It is important to consider the trade-offs associated with this reduction approach carefully. While it can help reduce memory constraints and improve computational efficiency, it may also lead to information loss and potential inaccuracies in risk assessment if crucial configurations are excluded due to the wrong approach choice. The uniqueness of features should be evaluated in order to use those approaches. The strategies outlined above are effective when dealing with datasets containing unique data, such as trajectory datasets. However, challenges arise when the dataset is less unique due to generalization and data protection measures. In such cases, the performance of the direct risk evaluation approach, reduced memory saving and entropy, and frequency are affected, highlighting the need for alternative strategies to address these challenges. Our work has some limitations that could be the subject of future research: the definition of a good set of attacks is still heavily human-dependent and does not take into account a precise analysis of the resources needed by the adversary. Therefore, the simulated attacks may be unrealistic. Selecting more realistic attacks may further improve the assessment efficiency, by pruning unreasonable simulations. Furthermore, future works using parallelization would be important for evaluating such cases. Another open challenge is determining the optimal percentage value to use. Selecting the percentage value for features with low entropy can impact the quality and accuracy of the risk assessment. Conceptualization, F.O.G., R.P., A.M. and C.R. ; Methodology, F.O.G., R.P., A.M., C.R. and J.E.M. ; Validation, A.M., C.R. and J.E.M. ; Formal analysis, F.O.G., R.P. and A.M.; Investigation, F.O.G., R.P.