摘 要
随着医疗信息化进程的加快，医疗数据在临床决策、科研分析及公共卫生管理中的价值日益凸显。然而，由于医疗数据涉及患者隐私和敏感信息，其共享与利用面临严峻的隐私泄露风险。为解决这一问题，本文设计了一种面向医疗数据的隐私保护共享系统，旨在实现数据可用不可见、保障数据主权与隐私安全。系统采用基于联邦学习与同态加密的混合架构，在保证多方协同计算的同时，避免原始数据的集中化存储。通过引入差分隐私机制对共享结果进行扰动处理，并结合区块链技术实现操作留痕与访问控制。实验结果表明，该系统在多个典型医疗数据分析任务中具有良好的准确性与计算效率，同时有效抵御了多种隐私攻击。本研究的主要创新在于构建了一个兼顾数据效用与隐私安全的可信共享框架，提出了动态差分隐私参数调整策略，提升了隐私保护的灵活性与适应性。研究成果对于推动医疗数据合规流通、促进跨机构协作具有重要意义。关键词：医疗数据隐私保护；联邦学习与同态加密；动态差分隐私；区块链访问控制；可信数据共享
ABSTRACT
With the acceleration of medical informatization, the value of medical data in clinical decision-making, scientific research analysis, and public health management has become increasingly prominent. However, due to the involvement of patient privacy and sensitive information, the sharing and utilization of medical data face significant risks of privacy leakage. To address this issue, this paper designs a privacy-preserving medical data sharing system aimed at achieving data usability without compromising visibility, while ensuring data sovereignty and privacy security. The system adopts a hybrid architecture based on federated learning and homomorphic encryption, enabling collaborative computation across multiple parties without centralized storage of raw data. By introducing a differential privacy mechanism to perturb the sharing results and integrating blockchain technology for operation traceability and access control, the system ensures transparency and security in data usage. Experimental results demonstrate that the system achieves high accuracy and computational efficiency across multiple typical medical data analysis tasks, while effectively resisting various privacy attacks. The main innovation of this study lies in the construction of a trustworthy sharing fr amework that balances data utility with privacy protection, along with the proposal of a dynamic differential privacy parameter adjustment strategy, which enhances the flexibility and adaptability of privacy protection. This research holds significant implications for promoting compliant circulation of medical data and facilitating cross-institutional collaboration.
Keywords: Medical Data Privacy Protection; Federated Learning And Homomorphic Encryption; Dynamic Differential Privacy; Blockchain Access Control; Trusted Data Sharing
目 录
摘 要 I
ABSTRACT II
绪 论 1
第一章 医疗数据隐私保护共享需求分析 2
1.1 医疗数据共享的现实挑战 2
1.2 隐私泄露风险与法律合规要求 2
1.3 系统设计的目标与核心价值 3
第二章 面向医疗数据的隐私保护机制设计 4
2.1 数据脱敏与匿名化技术选择 4
2.2 基于加密的访问控制策略 4
2.3 可信计算环境的构建路径 5
第三章 隐私保护共享系统架构与功能实现 6
3.1 系统整体架构设计原则 6
3.2 多方协同的数据共享流程 6
3.3 权限管理与审计追踪机制 7
第四章 系统性能评估与应用前景分析 8
4.1 实验设计与评估指标设定 8
4.2 安全性与效率的平衡测试 8
4.3 在智慧医疗中的应用展望 9
结 论 10
参考文献 11
致 谢 12
