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.

翻译：高效处理医学图像（如数字病理学中的全切片图像）对于及时诊断高风险疾病至关重要。然而，这需要先进的计算基础设施（例如用于深度学习推理的GPU服务器），而本地处理既耗时又成本高昂。此外，隐私问题进一步增加了远程云基础设施使用的复杂性。尽管已有研究探索了混合云中隐私与安全感知的分布式处理工作流调度，但隐私保护的数据分割、针对分割数据外包计算的服务分配优化，以及大规模医学图像的隐私评估等问题仍有待解决。本研究聚焦于在混合云环境中定制虚拟基础设施并调度图像处理服务，同时保障隐私。我们旨在最小化不可信节点的使用、降低货币成本，并在隐私、预算和截止时间约束下缩短执行时间。我们提出一种两阶段解决方案，包括：1）隐私保护数据分割算法；2）基于贪婪帕累托前沿的服务分配优化算法。通过真实与模拟数据实验，我们将所提方法与基线进行对比验证。结果表明，在隐私评估的个体隐私平均下限和信息增益方面，我们的隐私机制设计优于基线。此外，本方法可根据用户对最大不可信节点数、预算和时间阈值的偏好，获得多种帕累托最优分配方案。我们的解决方案通常主导基线方案，尤其在预算紧张时表现更优。具体而言，在总财务成本和时间成本上，本方法分别领先基线达85.2%和6.8%。