Synthetic Data: Use, History, Generation, Applications

Definition and scope of synthetic data

Synthetic data are artificially generated datasets that are not produced by real-world events, but instead created algorithmically to match the statistical distribution of sampled (real) data. They can be produced by computer simulations and other modeling systems, where the output approximates real-world behavior while remaining fully algorithmically generated. Synthetic data are used to validate mathematical models and to train machine learning models, especially when real data are scarce, expensive to label, or contain sensitive information. In privacy- and confidentiality-sensitive settings, synthetic data can be released or used for testing without exposing personal or confidential details, helping avoid privacy issues that arise from using real consumer information without permission. The scope of synthetic data includes a range of generation approaches (e.g., fitting statistical models to real data and then sampling from the fitted “synthesizer”), as well as applications such as fraud detection and intrusion testing, scientific research and baseline creation, and modern machine learning workflows (including transfer learning and large-scale dataset generation).

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Usefulness: meeting specific data needs

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