Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.13167 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910575142895616 |
|---|---|
| author | Volker, Thom Benjamin de Wolf, Peter-Paul van Kesteren, Erik-Jan |
| author_facet | Volker, Thom Benjamin de Wolf, Peter-Paul van Kesteren, Erik-Jan |
| contents | Synthetic data generation is a promising technique to facilitate the use of sensitive data while mitigating the risk of privacy breaches. However, for synthetic data to be useful in downstream analysis tasks, it needs to be of sufficient quality. Various methods have been proposed to measure the utility of synthetic data, but their results are often incomplete or even misleading. In this paper, we propose using density ratio estimation to improve quality evaluation for synthetic data, and thereby the quality of synthesized datasets. We show how this framework relates to and builds on existing measures, yielding global and local utility measures that are informative and easy to interpret. We develop an estimator which requires little to no manual tuning due to automatic selection of a nonparametric density ratio model. Through simulations, we find that density ratio estimation yields more accurate estimates of global utility than established procedures. A real-world data application demonstrates how the density ratio can guide refinements of synthesis models and can be used to improve downstream analyses. We conclude that density ratio estimation is a valuable tool in synthetic data generation workflows and provide these methods in the accessible open source R-package densityratio. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13167 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A density ratio framework for evaluating the utility of synthetic data Volker, Thom Benjamin de Wolf, Peter-Paul van Kesteren, Erik-Jan Machine Learning Synthetic data generation is a promising technique to facilitate the use of sensitive data while mitigating the risk of privacy breaches. However, for synthetic data to be useful in downstream analysis tasks, it needs to be of sufficient quality. Various methods have been proposed to measure the utility of synthetic data, but their results are often incomplete or even misleading. In this paper, we propose using density ratio estimation to improve quality evaluation for synthetic data, and thereby the quality of synthesized datasets. We show how this framework relates to and builds on existing measures, yielding global and local utility measures that are informative and easy to interpret. We develop an estimator which requires little to no manual tuning due to automatic selection of a nonparametric density ratio model. Through simulations, we find that density ratio estimation yields more accurate estimates of global utility than established procedures. A real-world data application demonstrates how the density ratio can guide refinements of synthesis models and can be used to improve downstream analyses. We conclude that density ratio estimation is a valuable tool in synthetic data generation workflows and provide these methods in the accessible open source R-package densityratio. |
| title | A density ratio framework for evaluating the utility of synthetic data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.13167 |