Post-processing Private Synthetic Data for Improving Utility on Selected Measures. (arXiv:2305.15538v1 [cs.LG])

Existing private synthetic data generation algorithms are agnostic to
downstream tasks. However, end users may have specific requirements that the
synthetic data must satisfy. Failure to meet these requirements could
significantly reduce the utility of the data for downstream use. We introduce a
post-processing technique that improves the utility of the synthetic data with
respect to measures selected by the end user, while preserving strong privacy
guarantees and dataset quality. Our technique involves resampling from the
synthetic data to filter …

algorithms data end may private requirements synthetic synthetic data utility