Synthetic Data Generation is the process of creating artificial data that mimics real-world data distributions. It is used to protect privacy, improve AI training datasets, and test machine learning models in scenarios where real data is scarce or sensitive.
Frequently asked questions
What is synthetic data generation?
Creating new records that preserve the statistical structure of real data without copying real individuals, so AI can train on realistic data that carries no real personal information.
How does synthetic data generation protect privacy?
When the generation process applies differential privacy, the output carries a formal guarantee that no individual record can be recovered from it.
When should you use synthetic data generation?
When real data is restricted, imbalanced, or too small: to expand coverage and unblock AI work without exposing source records.