Trajectory Privacy Protection Model Based on BiLSTM-GAN
[1]YAN Hongcan,ZHAO Yuting,LI Sijia,et al.Trajectory Privacy Protection Model Based on BiLSTM-GAN[J].Journal of Zhengzhou University (Engineering Science),2026,47(4):117-124.[doi:10.13705/j.issn.1671-6833.2026.04.015]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
47
Number of periods:
2026 Issue 4
Page number:
117-124
Column:
Public date:
2026-07-10
- Title:
- Trajectory Privacy Protection Model Based on BiLSTM-GAN
- Keywords:
- trajectory protection; hierarchical density-based spatial clustering of applications with noise ( HDB-SCAN) ; bidirectional long short-term memory network (BiLSTM) ; generative adversarial network (GAN) ; hidden Markov model (HMM) ; trajectory similarity
- CLC:
- TP309. 2U49
- Abstract:
- The exponential growth of mobile trajectory data in location‑based services has significantly increased the risk of user privacy leakage. It is urgent and necessary to make effective privacy protection mechanisms. To enhance the utility of trajectory data while ensuring privacy protection, a trajectory privacy protection model named TCI‑BiGAN was constructed based on BiLSTM‑GAN. The Bayesian optimization method was used to perform adaptive parameter tuning for hierarchical density‑based spatial clustering of applications with noise (HDBSCAN), to improve data processing efficiency and reduce trajectory redundancy. BiLSTM was embedded into both the generator and discriminator of the generative adversarial network to efficiently extract spatiotemporal features and capture dependencies of trajectory data through its contextual feature extraction capability, thereby to enhance the similarity between generated and real trajectories. A multivariate discrete hidden Markov model was applied for trajectory interpolation, to increase data completeness and utility. On the Foursquare NYC and T‑Drive real‑world datasets, the user trajectory linkage accuracy was reduced to 0.243 and 0.198 respectively, and the average Hausdorff distance between generated and real trajectories was decreased to 0.013 and 0.019 respectively.
- References:
-
[1] Liu Kai, Wang Jiaqin, Li Hantao. A review of vehicle trajectory prediction based on deep learning[J]. Journal of Zhengzhou University (Engineering Science), 2025, 46(5): 77‑89.［刘凯, 汪佳琴, 李汉涛. 基于深度学习的车辆轨迹预测研究综述［J］. 郑州大学学报(工学版), 2025, 46(5):77‑89.］
[2] Li Wenxuan, Wu Hao, Li Changsong. Survey of semantics‑based location privacy protection[J]. Journal of Computer Applications, 2023, 43(11): 3472‑3483.［李雯萱, 吴昊, 李昌松. 基于语义的位置隐私保护综述［J］. 计算机应用, 2023, 43(11): 3472‑3483.］
[3] Ashbrook D, Starner T. Using GPS to learn significant locations and predict movement across multiple users[J]. Personal and Ubiquitous Computing, 2003, 7(5): 275‑286.
[4] Ester M, Kriegel H P, Sander J, et al. A density‑based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. New York: ACM, 1996: 226‑231.
[5] Campello R J G B, Moulavi D, Sander J. Density‑based clustering based on hierarchical density estimates[C]//Advances in Knowledge Discovery and Data Mining. Cham: Springer, 2013: 160‑172.
[6] Yang Peiyu, Zhu Tongyu, Wan Xuejin, et al. Identifying significant places using multi‑day call detail records[C]//Proceedings of the 2014 IEEE 26th International Conference on Tools with Artificial Intelligence. Piscataway: IEEE, 2014: 360‑366.
[7] Shen Zihao, Tang Yuyu, Wang Hui, et al. Clustering and deep learning based trajectory privacy protection mechanism for Internet of Vehicles[J]. Journal of Zhejiang University (Engineering Science), 2024, 58(1): 20‑28.［申自浩, 唐雨雨, 王辉, 等. 基于聚类和深度学习的车联网轨迹隐私保护机制［J］. 浙江大学学报(工学版), 2024, 58(1): 20‑28.］
[8] Xu Zhenqiang, Wang Jiayao, Yang Weidong. Research progress in privacy‑preserving techniques for trajectory publication[J]. Journal of Geomatics Science and Technology, 2018, 35(1): 87‑93.［徐振强, 王家耀, 杨卫东. 面向轨迹数据发布的隐私保护技术研究进展［J］. 测绘科学技术学报, 2018, 35(1): 87‑93.］
[9] Kim J W, Jang B. Deep learning‑based privacy‑preserving framework for synthetic trajectory generation[J]. Journal of Network and Computer Applications, 2022, 206: 103459.
[10] Pan Jiaji, Yang Jingkang, Liu Yining. Dummy trajectory generation scheme based on deep learning[C]//Cyber‑space Safety and Security. Cham: Springer, 2019: 511‑523.
[11] Liu Xi, Chen Hanzhou, Andris C. TrajGANs: using generative adversarial networks for geo‑privacy protection of trajectory data (vision paper)[EB/OL]. [2026‑03‑01]. https://pal‑io.github.io/lopas2018/papers/LoPaS2018_Liu.pdf.
[12] Rao Jinmeng, Gao Song, Kang Yuhao, et al. LSTM‑Traj‑GAN: a deep learning approach to trajectory privacy protection[PP/OL]. V1. arXiv (2020‑06‑14)[2026‑03‑01]. https://doi.org/10.48550/arXiv.2006.10521.
[13] Choi S, Kim J, Yeo H. TrajGAIL: generating urban vehicle trajectories using generative adversarial imitation learning[J]. Transportation Research Part C: Emerging Technologies, 2021, 128: 103091.
[14] Wang Xingrui, Liu Xinyu, Lu Ziteng, et al. Large scale GPS trajectory generation using map based on two stage GAN[J]. Journal of Data Science, 2021: 126‑141.
[15] Yang Jingkang, Yu Xiaobo, Meng Weizhi, et al. Dummy trajectory generation scheme based on generative adversarial networks[J]. Neural Computing and Applications, 2023, 35(11): 8453‑8469.
[16] Shin J, Song Yeji, Ahn J, et al. TCAC‑GAN: synthetic trajectory generation model using auxiliary classifier generative adversarial network for improved protection of trajectory data[C]//Proceedings of the 2023 IEEE International Conference on Big Data and Smart Computing (BigComp). Piscataway: IEEE, 2023: 314‑315.
[17] Shin J, Song Yeji, Cheong Y Y, et al. Advanced trajectory privacy protection with attention mechanism and auxiliary classifier generative adversarial networks[C]//Proceedings of the 2024 International Conference on Information Networking (ICOIN). Piscataway: IEEE, 2024: 257‑261.
[18] Douglas D H, Peucker T K. Algorithms for the reduction of the number of points required to represent a digitized line or its caricature[J]. Cartographica, 1973, 10(2): 112‑122.
[19] Ma Shengjie, Wang Pei, Lee H. An enhanced hidden Markov model for map‑matching in pedestrian navigation[J]. Electronics, 2024, 13(9): 1685.
[20] Huang Zhiheng, Xu Wei, Yu Kai. Bidirectional LSTM‑CRF models for sequence tagging[PP/OL]. V1. arXiv (2015‑08‑09)[2026‑03‑01]. https://arxiv.org/abs/1508.01991.
[21] Goodfellow I J, Pouget‑Abadie J, Mirza M, et al. Generative adversarial nets[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems. New York: ACM, 2014: 2672‑2680.
[22] Goodfellow I, Bengio Y, Courville A. Deep learning[M]. Cambridge: MIT Press, 2016.
[23] Bishop C M. Pattern recognition and machine learning[M]. New York: Springer, 2006.
[24] Yang Dingqi, Zhang Daqing, Zheng V W, et al. Modeling user activity preference by leveraging user spatial‑temporal characteristics in LBSNs[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 45(1): 129‑142.
[25] Yuan Jing, Zheng Yu, Xie Xing, et al. Driving with knowledge from the physical world[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2011: 316‑324.
[26] Petry L M, Silva C L D, Esuli A, et al. MARC: a robust method for multiple‑aspect trajectory classification via space, time, and semantic embeddings[J]. International Journal of Geographical Information Science, 2020, 34(7): 1428‑1450.
[27] Huttenlocher D P, Klanderman G A, Rucklidge W J. Comparing images using the Hausdorff distance[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(9): 850‑863.
[28] Snoek J, Larochelle H, Adams R P. Practical Bayesian optimization of machine learning algorithms[J]. Advances in neural information processing systems, 2012, 4: 2960‑2968.
[29] Bergstra J, Bengio Y. Random search for hyper‑parameter optimization[J]. Journal of Machine Learning Research, 2012, 13: 281‑305.[30] Gao Song, Rao Jinmeng, Liu Xinyi, et al. Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of Twitter users[J]. Journal of Spatial Information Science, 2019(19): 105‑129.
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Last Update:
2026-09-22
