AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Acceso en línea: | |
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| _version_ | 1866916283927232512 |
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| author | McKenna, Ryan Mullins, Brett Sheldon, Daniel Miklau, Gerome |
| author_facet | McKenna, Ryan Mullins, Brett Sheldon, Daniel Miklau, Gerome |
| contents | We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a workload-adaptive algorithm within the paradigm of algorithms that first selects a set of queries, then privately measures those queries, and finally generates synthetic data from the noisy measurements. It uses a set of innovative features to iteratively select the most useful measurements, reflecting both their relevance to the workload and their value in approximating the input data. We also provide analytic expressions to bound per-query error with high probability which can be used to construct confidence intervals and inform users about the accuracy of generated data. We show empirically that AIM consistently outperforms a wide variety of existing mechanisms across a variety of experimental settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2201_12677 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data McKenna, Ryan Mullins, Brett Sheldon, Daniel Miklau, Gerome Databases We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a workload-adaptive algorithm within the paradigm of algorithms that first selects a set of queries, then privately measures those queries, and finally generates synthetic data from the noisy measurements. It uses a set of innovative features to iteratively select the most useful measurements, reflecting both their relevance to the workload and their value in approximating the input data. We also provide analytic expressions to bound per-query error with high probability which can be used to construct confidence intervals and inform users about the accuracy of generated data. We show empirically that AIM consistently outperforms a wide variety of existing mechanisms across a variety of experimental settings. |
| title | AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data |
| topic | Databases |
| url | https://arxiv.org/abs/2201.12677 |