  Machine Learning as a Service (MLaaS), such as Microsoft Azure, Amazon AWS,
offers an effective DNN model to complete the machine learning task for small
businesses and individuals who are restricted to the lacking data and computing
power. However, here comes an issue that user privacy is ex-posed to the MLaaS
server, since users need to upload their sensitive data to the MLaaS server. In
order to preserve their privacy, users can encrypt their data before uploading
it. This makes it difficult to run the DNN model because it is not designed for
running in ciphertext domain. In this paper, using the Paillier homomorphic
cryptosystem we present a new Privacy-Preserving Deep Neural Network model that
we called 2P-DNN. This model can fulfill the machine leaning task in ciphertext
domain. By using 2P-DNN, MLaaS is able to provide a Privacy-Preserving machine
learning ser-vice for users. We build our 2P-DNN model based on LeNet-5, and
test it with the encrypted MNIST dataset. The classification accuracy is more
than 97%, which is close to the accuracy of LeNet-5 running with the MNIST
dataset and higher than that of other existing Privacy-Preserving machine
learning models
