  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.

Machine Learning;Cryptography and Security