  Cloud-edge collaborative inference approach splits deep neural networks
(DNNs) into two parts that run collaboratively on resource-constrained edge
devices and cloud servers, aiming at minimizing inference latency and
protecting data privacy. However, even if the raw input data from edge devices
is not directly exposed to the cloud, state-of-the-art attacks targeting
collaborative inference are still able to reconstruct the raw private data from
the intermediate outputs of the exposed local models, introducing serious
privacy risks. In this paper, a secure privacy inference framework for
cloud-edge collaboration is proposed, termed CIS, which supports adaptively
partitioning the network according to the dynamically changing network
bandwidth and fully releases the computational power of edge devices. To
mitigate the influence introduced by private perturbation, CIS provides a way
to achieve differential privacy protection by adding refined noise to the
intermediate layer feature maps offloaded to the cloud. Meanwhile, with a given
total privacy budget, the budget is reasonably allocated by the size of the
feature graph rank generated by different convolution filters, which makes the
inference in the cloud robust to the perturbed data, thus effectively trade-off
the conflicting problem between privacy and availability. Finally, we construct
a real cloud-edge collaborative inference computing scenario to verify the
effectiveness of inference latency and model partitioning on
resource-constrained edge devices. Furthermore, the state-of-the-art cloud-edge
collaborative reconstruction attack is used to evaluate the practical
availability of the end-to-end privacy protection mechanism provided by CIS.
