差别隐私
建筑
联合学习
计算机科学
患者隐私
残差神经网络
隐私保护
差速器(机械装置)
互联网隐私
信息隐私
数据聚合器
计算机安全
深度学习
人工智能
数据挖掘
计算机网络
医疗保健
政治学
工程类
地理
无线传感器网络
法学
航空航天工程
考古
作者
M. Fares,Ahmed Mohamed Saad Emam Saad
出处
期刊:Cornell University - arXiv
日期:2024-12-01
标识
DOI:10.48550/arxiv.2412.00687
摘要
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.
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