Efficiency Boosts in Human Mobility Data Privacy Risk Assessment: Advancements within the PRUDEnce Framework  
[PERSON]  
1 Graduate Program on Computer Science, Department of Informatics and Statistics, Federal University of Santa Catarina (UPSC), Florianopolis 88040-370, SC, Brazil; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Computer Science, University of Pisa, 56126 Pisa, Italy; [EMAIL_ADDRESS]  
[PERSON]  
2 Department of Computer Science, University of Pisa, 56126 Pisa, Italy; [EMAIL_ADDRESS]  
[PERSON]  
4 The Institute of Information Science and Technologies (ISTI) of the National Research Council (CNR), 56124 Pisa, Italy; [EMAIL_ADDRESS]  
[PERSON]  
1 Graduate Program on Computer Science, Department of Informatics and Statistics, Federal University of Santa Catarina (UPSC), Florianopolis 88040-370, SC, Brazil; [EMAIL_ADDRESS]  
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for the processing of personal data. These laws assign data controllers and processors to handle data, ensuring data protection. One important step for data custodians is to perform a quantitative privacy risk data assessment. Numerous privacy risk assessment methodologies based on probability and frequency have been proposed for evaluating the privacy risk across various data types [4; 5; 6; 7; 8]. One of the challenges in privacy risk assessment, especially concerning re-identification, is the need to reduce the computational resources required for evaluating privacy risk. One of the most accurate methods for privacy risk assessment is the simulation of background knowledge-based attacks. When simulating this type of attack, generating the background knowledge representing the adversary's knowledge about its victims is a particularly complex task, requiring substantial computational resources. As the adversary's knowledge expands, the computational complexity increases exponentially until half of the maximum possible knowledge size. Previous studies, such as those by [PERSON] et al. [9] and [PERSON] et al. [10], have explored using machine learning algorithms to mitigate this computational load, particularly when new data become available. Both methods were successful in significantly reducing the overall processing time. However, an initial computation of risk using combinations remains essential to establish the training dataset for their approach, which means that the lengthy execution time is still a challenge that needs to be addressed. Due to the data size, the process becomes more complex when dealing with trajectory data. Background knowledge related to trajectories often consists of sequences of visited places or simply locations, representing places or visits the attacker is aware of regarding the victim's movement. As this knowledge grows, the number of potential background knowledge configurations to evaluate increases significantly. Each configuration requires risk assessment, further adding to the overall complexity of the process. The time required for this assessment depends on the dataset size, number of trajectories, and trajectory length. This work addresses computational challenges encountered in risk assessment analysis using PRUDEnce [11], a state-of-the-art privacy risk assessment framework for background knowledge-based attacks. We aim to develop strategies to mitigate the computational complexity of evaluating privacy risks in trajectory datasets. We explore the complexities arising from the high computational demands of re-identification risk assessment. We propose different computational improvements and optimization strategies to simplify the risk assessment process, enhance computational efficiency, and facilitate more scalable and accurate analyses.