论文标题
LSTM-Trajgan：一种深度学习方法，以保护轨迹隐私保护
LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection
论文作者
论文摘要
基于位置的服务的普遍性有助于个人级别轨迹数据的爆炸性增长，并引起了公众对隐私问题的担忧。在这项研究中，我们提出了一种新颖的LSTM-TRAJGAN方法，该方法是一种端到端的深度学习模型，旨在生成隐私的合成轨迹数据，以进行数据共享和发布。我们设计了损失度量函数trajloss，以测量模型训练和优化的轨迹相似性损失。该模型在实际语义轨迹数据集上的轨迹 - 用户链接任务上进行了评估。与其他常见的代码化方法相比，我们的模型可以更好地防止用户被重新识别，并且还保留了实际轨迹数据的基本空间，时间和主题特征。该模型可以更好地平衡轨迹隐私保护的有效性和空间和时间分析的实用性，从而为Geoai驱动的隐私保护提供了新的见解。
The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. We design a loss metric function TrajLoss to measure the trajectory similarity losses for model training and optimization. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset. Compared with other common geomasking methods, our model can better prevent users from being re-identified, and it also preserves essential spatial, temporal, and thematic characteristics of the real trajectory data. The model better balances the effectiveness of trajectory privacy protection and the utility for spatial and temporal analyses, which offers new insights into the GeoAI-powered privacy protection.
