  Deep Neural Network (DNN) has been showing great potential in kinds of
real-world applications such as fraud detection and distress prediction.
Meanwhile, data isolation has become a serious problem currently, i.e.,
different parties cannot share data with each other. To solve this issue, most
research leverages cryptographic techniques to train secure DNN models for
multi-parties without compromising their private data. Although such methods
have strong security guarantee, they are difficult to scale to deep networks
and large datasets due to its high communication and computation complexities.
To solve the scalability of the existing secure Deep Neural Network (DNN) in
data isolation scenarios, in this paper, we propose an industrial scale privacy
preserving neural network learning paradigm, which is secure against
semi-honest adversaries. Our main idea is to split the computation graph of DNN
into two parts, i.e., the computations related to private data are performed by
each party using cryptographic techniques, and the rest computations are done
by a neutral server with high computation ability. We also present a defender
mechanism for further privacy protection. We conduct experiments on real-world
fraud detection dataset and financial distress prediction dataset, the
encouraging results demonstrate the practicalness of our proposal.
