Differentially Private Synthetic Data with Private Density Estimation
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916238774501376 |
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| author | Bojkovic, Nikolija Loh, Po-Ling |
| author_facet | Bojkovic, Nikolija Loh, Po-Ling |
| contents | The need to analyze sensitive data, such as medical records or financial data, has created a critical research challenge in recent years. In this paper, we adopt the framework of differential privacy, and explore mechanisms for generating an entire dataset which accurately captures characteristics of the original data. We build upon the work of Boedihardjo et al, which laid the foundations for a new optimization-based algorithm for generating private synthetic data. Importantly, we adapt their algorithm by replacing a uniform sampling step with a private distribution estimator; this allows us to obtain better computational guarantees for discrete distributions, and develop a novel algorithm suitable for continuous distributions. We also explore applications of our work to several statistical tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04554 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Differentially Private Synthetic Data with Private Density Estimation Bojkovic, Nikolija Loh, Po-Ling Cryptography and Security Information Theory Machine Learning Statistics Theory 62G07 The need to analyze sensitive data, such as medical records or financial data, has created a critical research challenge in recent years. In this paper, we adopt the framework of differential privacy, and explore mechanisms for generating an entire dataset which accurately captures characteristics of the original data. We build upon the work of Boedihardjo et al, which laid the foundations for a new optimization-based algorithm for generating private synthetic data. Importantly, we adapt their algorithm by replacing a uniform sampling step with a private distribution estimator; this allows us to obtain better computational guarantees for discrete distributions, and develop a novel algorithm suitable for continuous distributions. We also explore applications of our work to several statistical tasks. |
| title | Differentially Private Synthetic Data with Private Density Estimation |
| topic | Cryptography and Security Information Theory Machine Learning Statistics Theory 62G07 |
| url | https://arxiv.org/abs/2405.04554 |