Reseach activities at ACM SIGSPATIAL2024
Research presentations and organizing GeoPrivacy Workshop at ACM SIGSPATIAL2024.
Publications & Talks
International Conference · 2024
Restoring Super-High Resolution GPS Mobility Data
Abstract
This paper presents a novel system for reconstructing high-resolution GPS trajectory data from truncated or synthetic low-resolution inputs, addressing the critical challenge of balancing data utility with privacy preservation in mobility applications. The system integrates transformer-based encoder-decoder models with graph convolutional networks (GCNs) to effectively capture both the temporal dependencies of trajectory data and the spatial relationships in road networks. By combining these techniques, the system is able to recover fine-grained trajectory details that are lost through data truncation or rounding, a common practice to protect user privacy. We evaluate the system on the Beijing trajectory dataset, demonstrating its superior performance over traditional map-matching algorithms and LSTM-based synthetic data generation methods. The proposed model achieves an average Fréchet distance of 0.198 km, significantly outperforming map-matching algorithms (0.632 km) and synthetic trajectory models (0.498 km). The results show that the system is not only capable of accurately reconstructing real-world trajectories but also generalizes effectively to synthetic data. These findings suggest that the system can be deployed in urban mobility applications, providing both high accuracy and robust privacy protection.
Research Note
This research aims to realize a novel system for reconstructing high-resolution GPS trajectory data from truncated or low-resolution inputs, addressing the challenge of balancing data utility with privacy preservation in mobility applications. The system integrates transformer-based encoder-decoder models with graph convolutional networks (GCNs) to effectively capture the temporal dependencies of trajectory data and spatial relationships in road networks. By leveraging these techniques, the system restores fine-grained trajectory details lost due to data truncation or rounding, which are common practices for privacy protection.
Published Paper
- Yonekura, H., Ozeki, R., Rizk, H., & Yamaguchi, H. (2024, October). Restoring Super-High Resolution GPS Mobility Data. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies (pp. 19-24). https://dl.acm.org/doi/abs/10.1145/3681768.3698501
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