Efficiency boosts in human mobility data privacy risk assessment: advancements within the PRUDEnce framework
Gomes F. O., Pellungrini R., Monreale A., Renso C., Martina J. E.
Privacy, Privacy risk, Privacy risk assessment, Mobility, Re-identification, Computation improvements; risk; trajectory
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.
Source: APPLIED SCIENCES, vol. 14 (issue 17)
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@article{oai:iris.cnr.it:20.500.14243/501085,
title = {Efficiency boosts in human mobility data privacy risk assessment: advancements within the PRUDEnce framework},
author = {Gomes F. O. and Pellungrini R. and Monreale A. and Renso C. and Martina J. E.},
year = {2024}
}
0000-0001-8541-0284
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—
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PNRR-M4C2-Investimento 1.3, Partenariato Esteso PE00000013-“FAIR-Future Artificial Intelligence Research”-Spoke 1 “Human-centered AI”, funded by the
PNRR-M4C2-Investimento 1.3, Partenariato Esteso PE00000013-“FAIR-Future Artificial Intelligence Research”-Spoke 1 “Human-centered AI”, funded by the
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