Differentially Private Synthetic High-dimensional Tabular Stream
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912010098180096 |
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| author | Kumar, Girish Strohmer, Thomas Vershynin, Roman |
| author_facet | Kumar, Girish Strohmer, Thomas Vershynin, Roman |
| contents | While differentially private synthetic data generation has been explored extensively in the literature, how to update this data in the future if the underlying private data changes is much less understood. We propose an algorithmic framework for streaming data that generates multiple synthetic datasets over time, tracking changes in the underlying private data. Our algorithm satisfies differential privacy for the entire input stream (continual differential privacy) and can be used for high-dimensional tabular data. Furthermore, we show the utility of our method via experiments on real-world datasets. The proposed algorithm builds upon a popular select, measure, fit, and iterate paradigm (used by offline synthetic data generation algorithms) and private counters for streams. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00322 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Differentially Private Synthetic High-dimensional Tabular Stream Kumar, Girish Strohmer, Thomas Vershynin, Roman Cryptography and Security Information Theory Machine Learning Statistics Theory While differentially private synthetic data generation has been explored extensively in the literature, how to update this data in the future if the underlying private data changes is much less understood. We propose an algorithmic framework for streaming data that generates multiple synthetic datasets over time, tracking changes in the underlying private data. Our algorithm satisfies differential privacy for the entire input stream (continual differential privacy) and can be used for high-dimensional tabular data. Furthermore, we show the utility of our method via experiments on real-world datasets. The proposed algorithm builds upon a popular select, measure, fit, and iterate paradigm (used by offline synthetic data generation algorithms) and private counters for streams. |
| title | Differentially Private Synthetic High-dimensional Tabular Stream |
| topic | Cryptography and Security Information Theory Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2409.00322 |