  We present a practical method for protecting data during the inference phase
of deep learning based on bipartite topology threat modeling and an interactive
adversarial deep network construction. We term this approach \emph{Privacy
Partitioning}. In the proposed framework, we split the machine learning models
and deploy a few layers into users' local devices, and the rest of the layers
into a remote server. We propose an approach to protect user's data during the
inference phase, while still achieve good classification accuracy.
  We conduct an experimental evaluation of this approach on benchmark datasets
of three computer vision tasks. The experimental results indicate that this
approach can be used to significantly attenuate the capacity for an adversary
with access to the state-of-the-art deep network's intermediate states to learn
privacy-sensitive inputs to the network. For example, we demonstrate that our
approach can prevent attackers from inferring the private attributes such as
gender from the Face image dataset without sacrificing the classification
accuracy of the original machine learning task such as Face Identification.
