PrivyNet: A Flexible Framework for Privacy-Preserving Deep Neural Network Training

Understand

Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints.

• Cloud-based training schemes provide beneficial services but suffer from potential privacy risks due to excessive user data collection.
• To enable cloud-based DNN training while protecting the data privacy simultaneously, we propose to leverage the intermediate representations of the data, which is achieved by splitting the DNNs and deploying them separately onto local platforms and the cloud.
• The local neural network (NN) is used to generate the feature representations.