May 15, 2024, 4:42 a.m. | Shun Takagi, Li Xiong, Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.08043v1 Announce Type: cross
Abstract: 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: …

abstract applications arxiv concerns cs.cr cs.lg data generative hierarchical human insights mobility network novel pandemic paper planning privacy raises resolution type urban urban planning

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