郑州大学学报（工学版）2026，Vol.47Issue(4)：117-124,8.DOI:10.13705/j.issn.1671-6833.2026.04.015
基于BiLSTM-GAN的轨迹隐私保护模型
Trajectory Privacy Protection Model Based on BiLSTM-GAN
摘要
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 en-hance 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 adap-tive 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 de-pendencies 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 in-terpolation,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.关键词
轨迹保护/基于层次密度的含噪声应用空间聚类/双向长短期记忆网络/生成对抗网络/隐马尔可夫模型/轨迹相似度Key words
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分类
信息技术与安全科学引用本文复制引用
阎红灿,赵雨婷,李思佳,辛禹池..基于BiLSTM-GAN的轨迹隐私保护模型[J].郑州大学学报（工学版）,2026,47(4):117-124,8.基金项目
河北省自然科学基金资助项目(G2024507002) （G2024507002）
