Zhiqing Hong
ABSTRACT
The availability of large amounts of data has driven the fast development of many research fields such as computer vision. However, the sharing of location data has been limited due to the concern of data privacy. Cellular location data contains user location information which reflects human mobility patterns. Therefore, in this study, we propose a novel Generative Adversarial Network (GAN) and apply the model to generate cellular location data as a case study for location-based sensing applications. The key insight of this study is that individual mobility correlates with user-specific information, e.g., age, gender. Therefore, to better capture the underlying pattern of human mobility, we design a soft-label conditional GAN which utilizes user-specific information to generate individual movement trajectories. This work plans to train a generator on a large real-world cellular location dataset and evaluate the synthetic data in terms of both utility and privacy.
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Digital Library
- Lin, Z., Jain, A., Wang, C., Fanti, G., and Sekar, V. Generating high-fidelity, synthetic time series datasets with doppelganger. arXiv preprint arXiv:1909.13403 (2019).Google Scholar
- Mirza, M., and Osindero, S. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014).Google Scholar
- Oliver, N., Lepri, B., Sterly, H., Lambiotte, R., Deletaille, S., De Nadai, M., Letouzé, E., Salah, A. A., Benjamins, R., Cattuto, C., et al. Mobile phone data for informing public health actions across the covid-19 pandemic life cycle, 2020.Google ScholarCross Ref
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Generating location data with generative adversarial networks for sensing applications: PhD forum abstract
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