SecDM: a secure and lossless human mobility prediction system

With the rapid development of deep neural network research, many deep neural network prediction services are provided for cloud service users. However, due to the untrusted nature of cloud computing, there are risks associated with this. To secure user data and cloud servers at the same time, we designed a secure prediction system, named SecDM (SecureDeepMove), which focuses on an attentional recurrent network for human mobility prediction. A neural network inference system that allows two parties to work together securely and efficiently without revealing any data is presented in this work. To design it, with the help of secret sharing technology, we first propose several secure, efficient, and lossless two-party protocols, which can securely calculate non-linear functions in the model, such as sigmoid, tanh, and softmax. In addition, a secure and effective strategy is introduced to maintain the accuracy of the calculation. Moreover, we also prove the security of our scheme in the semi-honest model. Finally, experimental results validate that the prediction result of our SecDM is not only as accurate as the non-privacy-preserving scheme but also highly efficient.