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Open AccessArticle
Efficiency Boosts in Human Mobility Data Privacy Risk Assessment: Advancements within the PRUDEnce Framework
by
Fernanda O. Gomes
Fernanda O. Gomes 1,2,*
,
Roberto Pellungrini
Roberto Pellungrini 3
,
Anna Monreale
Anna Monreale 2,
Chiara Renso
Chiara Renso 4 and
Jean E. Martina
Jean E. Martina 1
1
Graduate Program on Computer Science, Department of Informatics and Statistics, Federal University of Santa Catarina (UFSC), Florianópolis 88040-370, SC, Brazil
2
Department of Computer Science, University of Pisa, 56126 Pisa, Italy
3
Classe di Scienze—Scuola Normale Superiore, 56126 Pisa, Italy
4
The Institute of Information Science and Technologies (ISTI) of the National Research Council (CNR), 56124 Pisa, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(17), 8014; https://doi.org/10.3390/app14178014 (registering DOI)
Submission received: 24 July 2024
/
Revised: 3 September 2024
/
Accepted: 6 September 2024
/
Published: 7 September 2024
Abstract
With the exponential growth of mobility data generated by IoT, social networks, and mobile devices, there is a pressing need to address privacy concerns. Our work proposes methods to reduce the computation of privacy risk evaluation on mobility datasets, focusing on reducing background knowledge configurations and matching functions, and enhancing code performance. Leveraging the unique characteristics of trajectory data, we aim to minimize the size of combination sets and directly evaluate risk for trajectories with distinct values. Additionally, we optimize efficiency by storing essential information in memory to eliminate unnecessary computations. These approaches offer a more efficient and effective means of identifying and addressing privacy risks associated with diverse mobility datasets.
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MDPI and ACS Style
Gomes, F.O.; Pellungrini, R.; Monreale, A.; Renso, C.; Martina, J.E.
Efficiency Boosts in Human Mobility Data Privacy Risk Assessment: Advancements within the PRUDEnce Framework. Appl. Sci. 2024, 14, 8014.
https://doi.org/10.3390/app14178014
AMA Style
Gomes FO, Pellungrini R, Monreale A, Renso C, Martina JE.
Efficiency Boosts in Human Mobility Data Privacy Risk Assessment: Advancements within the PRUDEnce Framework. Applied Sciences. 2024; 14(17):8014.
https://doi.org/10.3390/app14178014
Chicago/Turabian Style
Gomes, Fernanda O., Roberto Pellungrini, Anna Monreale, Chiara Renso, and Jean E. Martina.
2024. "Efficiency Boosts in Human Mobility Data Privacy Risk Assessment: Advancements within the PRUDEnce Framework" Applied Sciences 14, no. 17: 8014.
https://doi.org/10.3390/app14178014
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