摘 要：
医疗数据共享在促进医疗健康领域的科研和服务提供方面扮演着重要角色，然而，数据安全和个人隐私保护问题一直是阻碍其广泛应用的主要难题。鉴于此，本论文探讨了隐私计算技术在医疗数据共享中的应用边界。我们首先介绍了隐私计算技术的概念以及主要的技术手段，如密码学、差分隐私、联邦学习等，并深入阐述了它们在医疗健康领域的具体应用。然后，我们分析了隐私计算技术在医疗数据保护中的优势和挑战，包括数据的安全性、数据的真实性、技术的复杂性等问题。接着，我们通过挖掘现有的医疗数据集和实例以及进行依赖隐私计算技术的模拟实验，确定了隐私计算技术在医疗数据共享中的应用边界。最后，我们发现，隐私计算技术对于保护医疗数据的安全具有巨大的潜力，但同时也存在一定的局限性，需要我们持续的研究和探索。整体而言，本研究有助于进一步理解隐私计算技术在医疗数据共享中的应用边界，并为未来的研究提供了有益的参考。
关键词：隐私计算技术；医疗数据共享；数据安全；差分隐私；联邦学习
Abstract:
Medical data sharing plays a pivotal role in advancing scientific research and service delivery within the healthcare sector. However, issues related to data security and personal privacy protection have been major obstacles hindering its widespread application. In light of this, this paper explores the application boundaries of privacy-preserving computation technologies in medical data sharing. We first introduce the concept of privacy-preserving computation technologies and their primary technical approaches, such as cryptography, differential privacy, federated learning, etc., and delve into their specific applications in the healthcare field. Subsequently, we analyze the advantages and challenges of privacy-preserving computation technologies in medical data protection, including issues related to data security, data authenticity, and technical complexity. By examining existing medical datasets and examples, as well as conducting simulation experiments relying on privacy-preserving computation technologies, we then determine the application boundaries of these technologies in medical data sharing. Finally, we find that while privacy-preserving computation technologies hold significant potential for safeguarding the security of medical data, they also possess certain limitations that necessitate ongoing research and exploration. Overall, this study contributes to a deeper understanding of the application boundaries of privacy-preserving computation technologies in medical data sharing and provides valuable references for future research.
Keywords: Privacy-preserving computation technologies; Medical data sharing; Data security; Differential privacy; Federated learning
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