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：人体移动数据为城市规划和疫情响应等许多应用提供了宝贵见解，但其使用也引发了隐私问题。本文介绍分层多分辨率网络（HRNet），这是一种专为在保证差分隐私的同时合成逼真人体移动数据而设计的新型深度生成模型。我们首先揭示了在差分隐私约束下学习人体移动数据所固有的关键难点。针对这些挑战，HRNet整合了三个组件：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。这些元素共同增强了模型在差分隐私约束下的能力。通过利用真实世界数据集进行的广泛对比实验，HRNet在平衡效用-隐私权衡方面展现出相比现有方法的显著改进。