Generate synthetic samples from tabular data
- Resource Type
- Working Paper
- Authors
- Banh, David; Huang, Alan
- Source
- Subject
- Computer Science - Machine Learning
Computer Science - Cryptography and Security
- Language
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.