  Internet of Things (IoT) devices and applications are being deployed in our
homes and workplaces. These devices often rely on continuous data collection to
feed machine learning models. However, this approach introduces several privacy
and efficiency challenges, as the service operator can perform unwanted
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 neural networks for cooperative,
privacy-preserving analytics. To this end, instead of performing the whole
operation on the cloud, we let an IoT device to run the initial layers of the
neural network, and then send the output to the cloud to feed the remaining
layers and produce the final result. In order to ensure that the user's device
contains no extra information except what is necessary for the main task and
preventing any secondary inference on the data, we introduce Siamese
fine-tuning. We 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. Our evaluations show that by
using Siamese fine-tuning and at a small processing cost, we can greatly reduce
the level of unnecessary, potentially sensitive information in the personal
data, and thus achieving the desired trade-off between utility, privacy, and
performance.
