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.

翻译：深入学习作为一种服务(DLaaS)已经受到深入研究,以方便更广泛地部署新兴的深层学习应用程序。然而,DLaaS可能会损害客户和云服务器的隐私。虽然通过建立加密原始技术提出了某些隐私保护深神经网络(DNN)的推断技术,但由于DNN模型和昂贵的加密原始技术的复杂性,计算效率方面的挑战没有得到妥善解决。在本文中,我们提议了一个新的隐私保护基于云的DNN推断框架(即“PROUD ”),这大大提高了计算效率。最后,我们对两个常用的数据集进行了广泛的实验,以验证PROUD的有效性和效率,这些数据集也超越了最新技术。