The idea of using differential privacy isn’t that linear. It’s possible to use differential privacy along with large number of outside data without compromising the quality of output and privacy. The whole thing depends on how the system scrambles user data in a way that becomes anonymous as well as how the system gets near perfect output from the training data (in machine learning training data is the source for making decisions).