HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929428337000448 |
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| author | Takagi, Shun Xiong, Li Kato, Fumiyuki Cao, Yang Yoshikawa, Masatoshi |
| author_facet | Takagi, Shun Xiong, Li Kato, Fumiyuki Cao, Yang Yoshikawa, Masatoshi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08043 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization Takagi, Shun Xiong, Li Kato, Fumiyuki Cao, Yang Yoshikawa, Masatoshi Cryptography and Security Machine Learning 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. |
| title | HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2405.08043 |