  Neural networks, with the capability to provide efficient predictive models,
have been widely used in medical, financial, and other fields, bringing great
convenience to our lives. However, the high accuracy of the model requires a
large amount of data from multiple parties, raising public concerns about
privacy. Privacy-preserving neural network based on multi-party computation is
one of the current methods used to provide model training and inference under
the premise of solving data privacy. In this study, we propose a new two-party
privacy-preserving neural network training and inference framework in which
privacy data is distributed to two non-colluding servers. We construct a
preprocessing protocol for mask generation, support and realize secret sharing
comparison on 2PC, and propose a new method to further reduce the communication
rounds. Based on the comparison protocol, we construct building blocks such as
division and exponential, and realize the process of training and inference
that no longer needs to convert between different types of secret sharings and
is entirely based on arithmetic secret sharing. Compared with the previous
works, our work obtains higher accuracy, which is very close to that of
plaintext training. While the accuracy has been improved, the runtime is
reduced, considering the online phase, our work is 5x faster than SecureML,
4.32-5.75x faster than SecureNN, and is very close to the current optimal 3PC
implementation, FALCON. For secure inference, as far as known knowledge is
concerned, we should be the current optimal 2PC implementation, which is 4-358x
faster than other works.
