An Algorithm for Streaming Differentially Private Data
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910311919910912 |
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| author | Kumar, Girish Strohmer, Thomas Vershynin, Roman |
| author_facet | Kumar, Girish Strohmer, Thomas Vershynin, Roman |
| contents | Much of the research in differential privacy has focused on offline applications with the assumption that all data is available at once. When these algorithms are applied in practice to streams where data is collected over time, this either violates the privacy guarantees or results in poor utility. We derive an algorithm for differentially private synthetic streaming data generation, especially curated towards spatial datasets. Furthermore, we provide a general framework for online selective counting among a collection of queries which forms a basis for many tasks such as query answering and synthetic data generation. The utility of our algorithm is verified on both real-world and simulated datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14577 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Algorithm for Streaming Differentially Private Data Kumar, Girish Strohmer, Thomas Vershynin, Roman Databases Information Theory Machine Learning Statistics Theory Much of the research in differential privacy has focused on offline applications with the assumption that all data is available at once. When these algorithms are applied in practice to streams where data is collected over time, this either violates the privacy guarantees or results in poor utility. We derive an algorithm for differentially private synthetic streaming data generation, especially curated towards spatial datasets. Furthermore, we provide a general framework for online selective counting among a collection of queries which forms a basis for many tasks such as query answering and synthetic data generation. The utility of our algorithm is verified on both real-world and simulated datasets. |
| title | An Algorithm for Streaming Differentially Private Data |
| topic | Databases Information Theory Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2401.14577 |