  Efficiently processing medical images, such as whole slide images in digital
pathology, is essential for timely diagnosing high-risk diseases. However, this
demands advanced computing infrastructure, e.g., GPU servers for deep learning
inferencing, and local processing is time-consuming and costly. Besides,
privacy concerns further complicate the employment of remote cloud
infrastructures. While previous research has explored privacy and
security-aware workflow scheduling in hybrid clouds for distributed processing,
privacy-preserving data splitting, optimizing the service allocation of
outsourcing computation on split data to the cloud, and privacy evaluation for
large medical images still need to be addressed. This study focuses on
tailoring a virtual infrastructure within a hybrid cloud environment and
scheduling the image processing services while preserving privacy. We aim to
minimize the use of untrusted nodes, lower monetary costs, and reduce execution
time under privacy, budget, and deadline requirements. We consider a two-phase
solution and develop 1) a privacy-preserving data splitting algorithm and 2) a
greedy Pareto front-based algorithm for optimizing the service allocation. We
conducted experiments with real and simulated data to validate and compare our
method with a baseline. The results show that our privacy mechanism design
outperforms the baseline regarding the average lower band on individual privacy
and information gain for privacy evaluation. In addition, our approach can
obtain various Pareto optimal-based allocations with users' preferences on the
maximum number of untrusted nodes, budget, and time threshold. Our solutions
often dominate the baseline's solution and are superior on a tight budget.
Specifically, our approach has been ahead of baseline, up to 85.2% and 6.8% in
terms of the total financial and time costs, respectively.
