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.

翻译：在集中式计算环境下，运行针对大规模医学图像的深度神经网络是一项资源密集且耗时的任务。将此类医学图像处理任务外包至混合云具有显著优势，例如可大幅缩短执行时间并降低经济成本。然而，由于隐私考量，在云端处理敏感医学图像仍面临挑战，这阻碍了其在众多实际应用中的部署。为克服这一难题，我们首先构建了隐私保护分布式系统模型的整体优化目标，即：在用户预算约束下，最小化攻击者在整个过程中可获取的私有信息量，同时降低最大执行时间与经济成本。我们提出了一种名为PriCE的新型隐私保护与成本效益优化方法来解决该多目标优化问题。我们以五个深度卷积神经网络推理集成作为工作流任务，针对医学图像伪影检测任务开展了大量模拟实验。实验结果表明，PriCE能通过基于图着色策略成功分割多种千兆像素级医学输入图像，在用户预算范围内实现理想的输出效用，并有效降低隐私风险、完工时间与经济成本。