Synthesizing Realistic Test Data without Breaking Privacy

We describe a dataset with a grammar, generate samples from it with a fuzzer, and train a decision-tree classifier to tell generated samples apart from the original data. Samples the classifier takes for originals seed the next round of generation, and the discriminator is retrained each round on the newest data.