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

Privacy-preserving Cloud-based DNN Inference