【Author】
Cheng, Wenzhi; Ou, Wei; Yin, Xiangdong; Yan, Wanqin; Liu, Dingwan; Liu, Chunyan
【Source】SECURITY AND COMMUNICATION NETWORKS
【Abstract】The collection and analysis of patient cases can effectively help researchers to extract case feature and to achieve the objectives of precision medicine, but it may cause privacy issues for patients. Although encryption is a good way to protect privacy, it is not conducive to the sharing and analysis of medical cases. In order to address this problem, this paper proposes a federated learning verification model, which combines blockchain technology, homomorphic encryption, and federated learning technology to effectively solve privacy issues. Moreover, we present a FL-EM-GMM Algorithm (Federated Learning Expectation Maximization Gaussian Mixture Model Algorithm), which can make model training without data exchange for protecting patient's privacy. Finally, we conducted experiments on the federated task of datasets from two organizations in our model system, where the data has the same sample ID with different subset features, and this system is capable of handling privacy and security issues. The results show that the model was trained by our system with better usability, security, and higher efficiency, which is compared with the model trained by traditional machine learning methods.
【摘要】患者病例的采集和分析可以有效帮助研究人员提取病例特征，达到精准医疗的目的，但可能会给患者带来隐私问题。加密虽然是保护隐私的好方法，但不利于医疗案例的分享和分析。为了解决这个问题，本文提出了一种联邦学习验证模型，它结合了区块链技术、同态加密和联邦学习技术，有效解决了隐私问题。此外，我们提出了一种 FL-EM-GMM 算法（Federated Learning Expectation Maximization Gaussian Mixture Model Algorithm），该算法可以在没有数据交换的情况下进行模型训练，以保护患者的隐私。最后，我们在模型系统中对来自两个组织的数据集的联合任务进行了实验，其中数据具有相同的样本 ID，具有不同的子集特征，并且该系统能够处理隐私和安全问题。结果表明，与传统机器学习方法训练的模型相比，我们的系统训练出的模型具有更好的可用性、安全性和更高的效率。
评论