Generate synthetic samples from tabular data

TL;DR

This paper discusses a method for generating synthetic tabular data to address privacy concerns, improve data sharing, and reduce costs associated with data collection and invasive procedures.

Contribution

It introduces a novel approach for creating statistically robust synthetic samples from tabular data to enhance privacy and data sharing practices.

Findings

Synthetic samples improve privacy preservation.

Method reduces costs of data collection.

Enhances data sharing without compromising privacy.

Abstract

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.