by generating new data that has different properties from the data you already had one thing that you can do is you can create differentially private data so suppose that you have something like medical records and you don't want to train a classifier on the medical records and then publish the classifier because someone might be able to reverse-engineer some of the medical records you trained on there's a paper from Casey greens lab that shows how you can train again using differential privacy and then the samples one again