  The huge computation demand of deep learning models and limited computation
resources on the edge devices calls for the cooperation between edge device and
cloud service by splitting the deep models into two halves. However,
transferring the intermediates results from the partial models between edge
device and cloud service makes the user privacy vulnerable since the attacker
can intercept the intermediate results and extract privacy information from
them. Existing research works rely on metrics that are either impractical or
insufficient to measure the effectiveness of privacy protection methods in the
above scenario, especially from the aspect of a single user. In this paper, we
first present a formal definition of the privacy protection problem in the
edge-cloud system running DNN models. Then, we analyze the-state-of-the-art
methods and point out the drawbacks of their methods, especially the evaluation
metrics such as the Mutual Information (MI). In addition, we perform several
experiments to demonstrate that although existing methods perform well under
MI, they are not effective enough to protect the privacy of a single user. To
address the drawbacks of the evaluation metrics, we propose two new metrics
that are more accurate to measure the effectiveness of privacy protection
methods. Finally, we highlight several potential research directions to
encourage future efforts addressing the privacy protection problem.
