摘要： 针对现有轨迹隐私保护方法存在轨迹效用性不高和隐私保护不充分的问题，提出一种基于Peephole LSTM的生成对抗网络轨迹隐私保护方案。该方案设计了融合窥孔链接机制的生成器模型，使各门控单元能够根据细胞状态的即时值自适应调整，从而更有效地感知上下文信息并捕捉轨迹序列内的依赖关系；判别器则利用长短期记忆网络判断合成轨迹的真伪。通过生成器和判别器的对抗训练，生成符合原有统计特征的轨迹数据，降低了攻击者识别用户的概率，从而增强用户轨迹信息的隐私保护。针对轨迹生成任务的多维特性，设计了新的轨迹损失函数，用以度量合成轨迹与真实轨迹在空间、时间、兴趣点类别上的相似度损失。通过在真实世界语义轨迹数据集Foursquare NYC上执行的轨迹-用户链接任务等实验证明，与LSTM-TrajGAN、TCAC-GAN等模型相比，本文方案生成的合成轨迹在降低重新识别概率的同时更好地保留了原始轨迹的空间、时间和兴趣点类别属性特征，从而有效平衡了轨迹数据的隐私性和效用性，确保其在时空分析和地理应用中的有效性。

Abstract: Addressing the issues of low trajectory utility and inadequate privacy protection in existing trajectory privacy protection methods, this paper proposes a generative adversarial network-based trajectory privacy protection scheme utilizing Peephole LSTM. This scheme designs a generator model that integrates a peephole link mechanism, enabling each gate unit to adaptively adjust based on the real-time values of cell states, thereby more effectively perceiving contextual information and capturing dependencies within trajectory sequences; the discriminator uses a long short-term memory network to determine the authenticity of synthesized trajectories. Through adversarial training between the generator and discriminator, trajectory data that aligns with the original statistical features is generated, reducing the probability of attackers identifying users and thereby enhancing the privacy protection of user trajectory information. Given the multidimensional nature of trajectory generation tasks, a new trajectory loss function is designed to measure the similarity loss between synthetic and real trajectories in terms of spatial, temporal, and point-of-interest category dimensions. Experiments conducted on the real-world semantic trajectory dataset Foursquare NYC, including trajectory-user linking tasks, demonstrate that compared to models such as LSTM-TrajGAN and TCAC-GAN, the synthetic trajectories generated by this approach not only reduce the probability of re-identification but also better preserve the spatial, temporal, and POI category attribute features of the original trajectories. This effectively balances the privacy and utility of trajectory data, ensuring its effectiveness in spatio-temporal analysis and geospatial applications.