Generate synthetic samples from tabular data
This addresses privacy and data sharing issues in domains handling sensitive tabular data, but appears incremental as it builds on existing synthetic data generation methods.
The paper tackles the problem of generating synthetic tabular data to reduce costs, invasive procedures, and privacy risks, resulting in statistically robust samples that serve as temporary replacements for sensitive data.
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.