医疗行业应用中用于保护数据隐私的同态加密
Homomorphic Encryption in Healthcare Industry Applications for Protecting Data Privacy
AI总结：
本文研究将全同态加密应用于医疗诊断神经网络与工业质量控制两类场景，通过框架选型和实验评估其性能、资源开销与实际可行性。
摘要：
本研究聚焦于两个不同的用例——工业场景中的质量控制（Quality Control，QC）方法和用于医疗诊断的神经网络（Neural Network，NN）算法——考察了将全同态加密（Fully Homomorphic Encryption，FHE）纳入医疗保健行业实际应用的情况。我们通过大量测试和分析，评估了在这些环境中部署FHE的性能、资源需求和可行性，重点展示了FHE工具方面取得的进展，以及在弥合概念研究与实际应用之间差距时仍面临的障碍。我们首先描述所研究的具体案例和信任模型。在选择最适合行业开发的两个FHE框架后，我们评估了在第一个场景（即质量控制算法）中实现这两个FHE框架各自所需的资源和性能要求。最后，我们的研究结果展示了在FHE环境中实现两个用例——复杂NN模型和简单QC算法——时的有效性和资源消耗。
英文摘要：
Focussing on two different use cases-Quality Control methods in industrial contexts and Neural Network algorithms for healthcare diagnostics-this research investigates the inclusion of Fully Homomorphic Encryption into real-world applications in the healthcare sector. We evaluate the performance, resource requirements, and viability of deploying FHE in these settings through extensive testing and analysis, highlighting the progress made in FHE tooling and the obstacles still facing addressing the gap between conceptual research and practical applications. We start our research by describing the specific case study and trust model were working with. Choosing the two FHE frameworks most appropriate for industry development, we assess the resources and performance requirements for implementing each of the two FHE frameworks in the first scenario, Quality Control algorithms. In conclusion, our findings demonstrate the effectiveness and resource consumption of the two use cases-complex NN models and simple QC algorithms-when implemented in an FHE setting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
