用隐私保护框架提升医疗影像联邦学习的准确率与安全性
Towards Privacy-Preserving Medical Imaging: Federated Learning with Differential Privacy and Secure Aggregation Using a Modified ResNet Architecture
- 结合差分隐私与安全多方计算，构建医疗图像分类的隐私保护联邦学习框架
- 在BloodMNIST数据集上达到接近非私有模型的准确率，优于传统方法
- 专为差分隐私优化的DPResNet架构，适合医疗数据隐私敏感场景
针对医疗数据隐私日益增长的担忧，本研究提出一种融合本地差分隐私与基于安全多方计算的安全聚合的联邦学习框架，用于医学图像分类。进一步提出了专为差分隐私优化的DPResNet模型架构。利用BloodMNIST基准数据集，模拟不同医院间的真实数据共享环境，应对联邦医疗数据带来的独特隐私挑战。实验结果表明，所提隐私保护联邦模型在保持严格数据保密性的前提下，性能接近非私有模型，显著优于传统方法。该方案提升了医疗数据管理的隐私性、效率与可靠性，为患者、医疗机构及整个医疗生态带来实质性益处。
原文摘要 · Abstract (English)
With increasing concerns over privacy in healthcare, especially for sensitive medical data, this research introduces a federated learning framework that combines local differential privacy and secure aggregation using Secure Multi-Party Computation for medical image classification. Further, we propose DPResNet, a modified ResNet architecture optimized for differential privacy. Leveraging the BloodMNIST benchmark dataset, we simulate a realistic data-sharing environment across different hospitals, addressing the distinct privacy challenges posed by federated healthcare data. Experimental results indicate that our privacy-preserving federated model achieves accuracy levels close to non-private models, surpassing traditional approaches while maintaining strict data confidentiality. By enhancing the privacy, efficiency, and reliability of healthcare data management, our approach offers substantial benefits to patients, healthcare providers, and the broader healthcare ecosystem.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。