Generating new samples from data sets can mitigate extra expensive operations, increased invasive procedures, and mitigate privacy issues. These novel samples that are statistically robust can be used as a temporary and intermediate replacement when privacy is a concern. This method can enable better data sharing practices without problems relating to identification issues or biases that are flaws for an adversarial attack.
@article{arxiv.2209.06113,
title = {Generate synthetic samples from tabular data},
author = {David Banh and Alan Huang},
journal= {arXiv preprint arXiv:2209.06113},
year = {2022}
}