I got your point and it sounds good, but let's take your example of the shipping addresses. Let's say you want to develop a model that helps finding the best route for an ups driver. The real data may contain addresses all based in New York because your customers business is located there. The fake data contains addresses based all around New York. I guess the model you train with the fake data won't be any helpful because instead the driver can do 5 locations in a hour, he has to drive 5 hours for the same amount. Gretle needs to know the context of the data and the importance of relationship between the data rows. A pure anonymisation won't be enough.