PySyft and the Emergence of Private Deep Learning

Trust is a key factor in the implementation of deep learning applications. From training to optimization, the lifecycle of a deep learning model is tied to trusted data exchanges between different parties. That dynamic is certainly effective for a lab environment but results are vulnerable to all sorts of security attacks that manipulate the trusted relationships among the different participants in a model. Let's take the example of a credit scoring model that uses a financial transaction to classify the credit risk of a specific customer. The traditional mechanisms for training or optimizing a model assume that the entities performing those actions will have full access to those financial datasets which opens the door to all sorts of privacy risks.