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.

翻译：人类移动数据为城市规划与疫情应对等诸多应用提供了宝贵洞见，但其使用亦引发隐私担忧。本文提出分层多分辨率网络（HRNet），这是一种专门设计用于合成真实人类移动数据并保证差分隐私的新型深度生成模型。我们首先识别了在差分隐私约束下学习人类移动数据所固有的关键难点。针对这些挑战，HRNet整合了三个核心组件：分层位置编码机制、跨多分辨率的多任务学习以及隐私预训练。这些要素共同增强了模型在差分隐私约束下的学习能力。通过基于真实数据集的广泛对比实验，HRNet在效用与隐私的权衡平衡方面展现出相较于现有方法的显著提升。