Privacy-preserving data release leveraging optimal transport and particle gradient descent
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913449766813696 |
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| author | Donhauser, Konstantin Abad, Javier Hulkund, Neha Yang, Fanny |
| author_facet | Donhauser, Konstantin Abad, Javier Hulkund, Neha Yang, Fanny |
| contents | We present a novel approach for differentially private data synthesis of protected tabular datasets, a relevant task in highly sensitive domains such as healthcare and government. Current state-of-the-art methods predominantly use marginal-based approaches, where a dataset is generated from private estimates of the marginals. In this paper, we introduce PrivPGD, a new generation method for marginal-based private data synthesis, leveraging tools from optimal transport and particle gradient descent. Our algorithm outperforms existing methods on a large range of datasets while being highly scalable and offering the flexibility to incorporate additional domain-specific constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17823 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Privacy-preserving data release leveraging optimal transport and particle gradient descent Donhauser, Konstantin Abad, Javier Hulkund, Neha Yang, Fanny Machine Learning Cryptography and Security We present a novel approach for differentially private data synthesis of protected tabular datasets, a relevant task in highly sensitive domains such as healthcare and government. Current state-of-the-art methods predominantly use marginal-based approaches, where a dataset is generated from private estimates of the marginals. In this paper, we introduce PrivPGD, a new generation method for marginal-based private data synthesis, leveraging tools from optimal transport and particle gradient descent. Our algorithm outperforms existing methods on a large range of datasets while being highly scalable and offering the flexibility to incorporate additional domain-specific constraints. |
| title | Privacy-preserving data release leveraging optimal transport and particle gradient descent |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2401.17823 |