HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization
This work addresses privacy concerns in using human mobility data for applications like urban planning, but it is incremental as it builds on existing differential privacy and generative modeling approaches.
The paper tackled the problem of synthesizing realistic human mobility data under differential privacy constraints, introducing HRNet, which improved the utility-privacy trade-off compared to existing methods.
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.