Title: A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics,
Abstract: Deep Neural Networks are increasingly being used in a variety of machine
learning applications applied to user data on the cloud. However, this approach
introduces a number of privacy and efficiency challenges, as the cloud operator
can perform secondary inferences on the available data. Recently, advances in
edge processing have paved the way for more efficient, and private, data
processing at the source for simple tasks and lighter models, though they
remain a challenge for larger, and more complicated models. In this paper, we
present a hybrid approach for breaking down large, complex deep models for
cooperative, privacy-preserving analytics. We do this by breaking down the
popular deep architectures and fine-tune them in a suitable way. We then
evaluate the privacy benefits of this approach based on the information exposed
to the cloud service. We also assess the local inference cost of different
layers on a modern handset for mobile applications. Our evaluations show that
by using certain kind of fine-tuning and embedding techniques and at a small
processing cost, we can greatly reduce the level of information available to
unintended tasks applied to the data features on the cloud, and hence achieving
the desired tradeoff between privacy and performance.