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Gjoreski_2023_IEEE_Access
Journal article

GANs for privacy-aware mobility modeling

  • 2023
Published in:
  • IEEE Access. - 2023, vol. 11, p. 29250 - 29262
Deep learning
Generative adversarial networks
Location data
Machine learning
Mobility modeling
Privacy
English Human mobility modeling is crucial for many facets of our society, including disease transmission modeling and urban planning. The explosion of mobility data prompted the application of deep learning to human mobility. Along with the growth of research interest, there is also increasing privacy concern. This study first examines the cutting-edge approaches for trajectory generation, classification, and next-location prediction. Second, we propose a novel privacy-aware approach for predicting next-week trajectories. The approach is based on two modules, a Generative Adversarial Network used for generating synthetic trajectories and a deep learning model for user identification which safeguards privacy. These two modules are combined with a next-week trajectory predictor that uses privacy-aware synthetic data. The experiments on two real-life datasets show that the generator creates trajectories similar to the real ones yet different enough to safeguard privacy. The low user-recognition recognition accuracy of models trained on the generated data demonstrates privacy awareness. Statistical tests confirm no significant difference between the original and the generated trajectories. We further demonstrate the utility of the synthetic data by predicting week-ahead trajectories based on the synthetic trajectories. Our study shows how privacy and utility can be managed jointly using the proposed privacy-aware approach.
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Language
  • English
Classification
Computer science and technology
License
CC BY
Open access status
gold
Identifiers
Persistent URL
https://n2t.net/ark:/12658/srd1330227
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