Google brings differential privacy to third-party ML developers using TensorFlow

Ahead of the 2019 TensorFlow Dev Summit, Google is announcing a new way for third-party developers to adopt differential privacy when training machine learning models. TensorFlow Privacy is designed to be easy to implement for developers already using the popular open-source ML library. The goal (via The Verge) of differential privacy for machine learning is to only "encode general patterns rather than facts about specific training examples." This allows user data to remain private, while the system overall still learns and can advance from general behavior. In particular, when training on users' data, those techniques offer strong mathematical guarantees that models do not learn or remember the details about any specific user.