  Running deep neural networks for large medical images is a resource-hungry
and time-consuming task with centralized computing. Outsourcing such medical
image processing tasks to hybrid clouds has benefits, such as a significant
reduction of execution time and monetary cost. However, due to privacy
concerns, it is still challenging to process sensitive medical images over
clouds, which would hinder their deployment in many real-world applications. To
overcome this, we first formulate the overall optimization objectives of the
privacy-preserving distributed system model, i.e., minimizing the amount of
information about the private data learned by the adversaries throughout the
process, reducing the maximum execution time and cost under the user budget
constraint. We propose a novel privacy-preserving and cost-effective method
called PriCE to solve this multi-objective optimization problem. We performed
extensive simulation experiments for artifact detection tasks on medical images
using an ensemble of five deep convolutional neural network inferences as the
workflow task. Experimental results show that PriCE successfully splits a wide
range of input gigapixel medical images with graph-coloring-based strategies,
yielding desired output utility and lowering the privacy risk, makespan, and
monetary cost under user's budget.
