Synthetic data refers to artificially generated data that mimics real-world data while preserving privacy and security. It is used in AI training, testing environments, and data augmentation to enhance model performance.
Frequently asked questions
What is synthetic data used for?
Expanding coverage for rare cases, balancing skewed datasets, and enabling AI work on data that is restricted or too small — without exposing real records.
Is synthetic data private?
When generated with differential privacy, synthetic outputs carry a formal mathematical guarantee that no individual record can be reverse-engineered from them.
How is synthetic data generated?
A model learns the statistical structure of source data and produces new records that preserve those patterns; differential privacy applied during generation keeps the output privacy-safe.