@inproceedings{9967c3a770ed40cf8b7aca6be3545505,
title = "Traj-MergeGAN: A Trajectory Privacy Preservation Model Based on Generative Adversarial Network",
abstract = "Nowadays, with the rapid development of location-based services, individual trajectory data is collected for various traffic related applications. However, while we are benefiting from these services, the trajectory data may contain lots of private information and privacy issues need to be carefully handled. In this paper, we propose a deep learning model named Traj-MergeGAN, which can generate synthetic trajectory from original trajectory. The generated trajectory can not only protect individual privacy, but also maintain data quality for other downstream applications. Furthermore, we conduct overall experiments to prove the advantages of our model.",
keywords = "Deep Learning, Generative Adversarial Network, Trajectory Privacy",
author = "Lida Guo and Zimeng Li and Jingyuan Wang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 17th International Conference on Knowledge Science, Engineering and Management, KSEM 2024 ; Conference date: 16-08-2024 Through 18-08-2024",
year = "2024",
doi = "10.1007/978-981-97-5498-4\_28",
language = "英语",
isbn = "9789819754977",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "361--372",
editor = "Cungeng Cao and Huajun Chen and Liang Zhao and Junaid Arshad and Yonghao Wang and Taufiq Asyhari",
booktitle = "Knowledge Science, Engineering and Management - 17th International Conference, KSEM 2024, Proceedings",
address = "德国",
}