HRNet：用于人类移动数据合成的差分隐私分层多分辨率网络
HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization
AI总结：
针对人类移动数据合成中的隐私问题，本文提出差分隐私分层多分辨率网络HRNet，通过分层编码、多任务学习和私有预训练，在真实数据集上显著提升了效用-隐私权衡。
摘要：
人类移动数据为城市规划和疫情应对等许多应用提供了宝贵的见解，但其使用也引发了隐私担忧。在本文中，我们引入了分层多分辨率网络（HRNet），这是一种新颖的深度生成模型，专门设计用于在保证差分隐私的同时合成逼真的人类移动数据。我们首先识别了在差分隐私下学习人类移动数据所固有的关键困难。针对这些挑战，HRNet整合了三个组成部分：分层位置编码机制、跨多分辨率的多任务学习以及私有预训练。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大量对比实验，HRNet在平衡效用-隐私权衡方面表现出比现有方法显著的改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
