  The increasing demand for on-device deep learning services calls for a highly
efficient manner to deploy deep neural networks (DNNs) on mobile devices with
limited capacity. The cloud-based solution is a promising approach to enabling
deep learning applications on mobile devices where the large portions of a DNN
are offloaded to the cloud. However, revealing data to the cloud leads to
potential privacy risk. To benefit from the cloud data center without the
privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN
which partitions the DNN across mobile devices and cloud data centers. A simple
data transformation is performed on the mobile device, while the
resource-hungry training and the complex inference rely on the cloud data
center. To protect the sensitive information, a lightweight privacy-preserving
mechanism consisting of arbitrary data nullification and random noise addition
is introduced, which provides strong privacy guarantee. A rigorous privacy
budget analysis is given. Nonetheless, the private perturbation to the original
data inevitably has a negative impact on the performance of further inference
on the cloud side. To mitigate this influence, we propose a noisy training
method to enhance the cloud-side network robustness to perturbed data. Through
the sophisticated design, ARDEN can not only preserve privacy but also improve
the inference performance. To validate the proposed ARDEN, a series of
experiments based on three image datasets and a real mobile application are
conducted. The experimental results demonstrate the effectiveness of ARDEN.
Finally, we implement ARDEN on a demo system to verify its practicality.
