Synthetic Data refers to artificially generated data that simulates real-world data but does not contain personally identifiable information (PII) or sensitive data. It is created using generative models and techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), or rule-based algorithms.
Benefits of Synthetic Data
- Synthetic Data allows for the safe and risk-free handling of data, as it does not contain any personally identifiable information or sensitive data.
- It provides a privacy-preserving solution, allowing organizations to share and collaborate on data without compromising individual privacy.
- Synthetic data can be generated in large quantities, enabling organizations to overcome limitations in data availability and size.
- It offers a cost-effective alternative to collecting and annotating real data, as synthetic data can be generated with minimal resources.
- Synthetic data can be customized to simulate various scenarios and data distributions, enhancing the diversity and representativeness of training data for machine learning models.