DLBox: New Model Training Framework for Protecting Training Data

—Sharing training data for deep learning raises critical concerns about data leakage, as third-party AI developers take full control over the data once it is handed over to them. The problem becomes even worse if the model trained using the data should be returned to the third-party AI developers—e.g., healthcare startup training its own model using the medical data rented from a hospital. In this case, the malicious developers can easily leak the training data through the model as he can construct an arbitrary data flow between them—e.g., directly encoding raw training data into the model, or stealthily biasing the model to resemble the training data. However, current model training frameworks do not provide any protection to prevent such training data leakage, allowing the untrusted AI developers to leak the data without any restriction. This paper proposes DLB OX , a new model training framework to minimize the attack vectors raised by untrusted AI developers.