Synthetic data generation is better than traditional anonymization methods. Instead of changing and in the process destroying an existing dataset, synthetic data is generated from scratch. First, a deep neural network learns all the structures and patterns in the actual data. After the training, the model uses this knowledge to generate new synthetic data. This artificially generated data is highly representative, yet completely anonymous. It does not contain any one-to-one relationships to actual data subjects, eliminating the risk of re-identification.