What is Synthetic Data?
Synthetic data is artificially generated data that statistically resembles real data but contains no actual personal information, useful for testing, development, and analytics.
Synthetic data is generated algorithmically to replicate the statistical properties, patterns, and relationships of real datasets without containing any actual personal data. Properly generated synthetic data preserves data utility for testing, model training, and analytics while eliminating privacy risk.
Synthetic data generation has become a valuable privacy-enhancing technology, particularly for creating realistic test environments, training machine learning models, and sharing data across organizational boundaries. However, synthesis quality must be validated to ensure statistical fidelity and absence of memorized real records.
Relevant Regulations
How IQWorks Helps
Related Terms
Data Anonymization
Anonymization irreversibly transforms personal data so that individuals can no longer be identified, even by the data controller, removing the data from privacy regulation scope.
Differential Privacy
Differential privacy is a mathematical framework that adds calibrated noise to data or query results, enabling statistical analysis while providing provable privacy guarantees for individuals.
Data Masking
Data masking replaces sensitive data with realistic but fictitious values, protecting privacy while maintaining data utility for testing, development, and analytics.
Privacy-Enhancing Technologies (PETs)
PETs are technologies designed to protect personal data privacy while enabling data processing, analysis, and sharing for legitimate purposes.