  Deep learning as a service (DLaaS) has been intensively studied to facilitate
the wider deployment of the emerging deep learning applications. However, DLaaS
may compromise the privacy of both clients and cloud servers. Although some
privacy preserving deep neural network (DNN) based inference techniques have
been proposed by composing cryptographic primitives, the challenges on
computational efficiency have not been well-addressed due to the complexity of
DNN models and expensive cryptographic primitives. In this paper, we propose a
novel privacy preserving cloud-based DNN inference framework (namely, "PROUD"),
which greatly improves the computational efficiency. Finally, we conduct
extensive experiments on two commonly-used datasets to validate both
effectiveness and efficiency for the PROUD, which also outperforms the
state-of-the-art techniques.
