Human mobility data offers valuable insights for many applications such as urban planning and pandemic response, but its use also raises privacy concerns. In this paper, we introduce the Hierarchical and Multi-Resolution Network (HRNet), a novel deep generative model specifically designed to synthesize realistic human mobility data while guaranteeing differential privacy. We first identify the key difficulties inherent in learning human mobility data under differential privacy. In response to these challenges, HRNet integrates three components: a hierarchical location encoding mechanism, multi-task learning across multiple resolutions, and private pre-training. These elements collectively enhance the model's ability under the constraints of differential privacy. Through extensive comparative experiments utilizing a real-world dataset, HRNet demonstrates a marked improvement over existing methods in balancing the utility-privacy trade-off.

介绍了一种专为合成真实人类流动数据而保证差分隐私的新型深层生成模型（HRNet），通过多任务学习、多分辨率、隐私预训练等多种组件来增强模型在差分隐私约束下的能力。在真实数据集上的广泛比较实验证明，HRNet在平衡效用与隐私权衡方面相比现有方法有显著改进。

HRNet：差分隐私层次和多分辨率网络在人类流动数据合成中的应用