Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

作     者：Fang Hu Siyi Qiu Xiaolian Yang ChaoleiWu Miguel Baptista Nunes Hui Chen 

作者机构：College of Information EngineeringHubei University of Chinese MedicineWuhan430065China College of Information EngineeringZhongnan University of Economics and LawWuhan430073China School of Advanced TechnologiesXi’an Jiaotong-Liverpool UniversitySuzhou215400China School of Information ManagementCentral China Normal UniversityWuhan430079China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第80卷第8期

页      面：2897-2915页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0816[工学-测绘科学与技术] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：We are thankful for the funding support fromthe Science and Technology Projects of the National Archives Administration of China(Grant Number 2022-R-031) the Fundamental Research Funds for the Central Universities,Central China Normal University(Grant Number CCNU24CG014) 

主　　题：Blockchain technique federated learning healthcare and medical data collaboration service privacy preservation 

摘      要：As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,*** guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed *** system is composed of three layers:Data extraction and storage,data management,and data *** on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and *** improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training ***,healthcare and medical data collaboration services in real-world scenarios have been discussed and *** further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image *** model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),***,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.