  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. To avoid local training and protect data
privacy, the local NN is derived from pre-trained NNs. The cloud NN is then
trained based on the extracted intermediate representations for the target
learning task. We validate the idea of DNN splitting by characterizing the
dependency of privacy loss and classification accuracy on the local NN topology
for a convolutional NN (CNN) based image classification task. Based on the
characterization, we further propose PrivyNet to determine the local NN
topology, which optimizes the accuracy of the target learning task under the
constraints on privacy loss, local computation, and storage. The efficiency and
effectiveness of PrivyNet are demonstrated with the CIFAR-10 dataset.
