基于联盟区块链的医疗健康数据安全模型

传统医疗信息系统中总是存在医疗健康数据安全存储难和共享难的问题,不同身份的人员在访问和共享医疗健康数据时都受到比较严格的限制,且验证身份和数据的真实性需要大量的资源和时间。针对传统医疗信息系统中存在的存储集中、共享安全性低和达成一致困难等问题,提出了一个基于联盟区块链的医疗健康数据安全模型。该模型根据目前的医疗资源分布情况将医疗机构划分等级,使用股份授权证明机制(Delegate Proot of Stake,DPOS)和实用拜占庭机制(Practical Byzantine Fautt Tolerante,PBFT)结合的混合共识机制保证在没有中心节点的情况下联盟中医疗机构可以快速达成一致,共享医疗健康数据;并根据区块链去中心、安全可信和防止篡改等特点,将数据记录及其他重要信息存储在区块链上,而将完整医疗数据加密存储在分布式数据库(Distributed Database,DDB)中,在安全存储用户医疗健康数据的同时,提高了数据在各医疗机构间的共享效率。安全性分析表明,该模型在容错范围内可以保护医疗健康数据,防止其被篡改和共谋;一致性分析表明,该模型有99%的概率保证联盟中医疗机构达成共识并共享医疗数据。

Medical Health Data Security Model Based on Alliance Blockchain

In traditional medical information system,medical health data security storage and sharing have been becoming a challenging task.There are many restrictions in process of health data accessing and sharing for different people of identity,which spends a lot of resources and time on identity verification and data authentication.Aiming at these problems such as storage of the high concentration,unreliable data sharing security and the difficulty of reaching agreement,this paper proposed an alliance blockchain-based medical health data security model.According to the distribution of medical resources in reality,the medical institutions are ranked in the security model,and then combine DPOS with PBFT to ensure that the medical institutions can reach an agreement rapidly without a central node and share medical data in alliance.The security model has the advantages of decentralization,high security and tamper resistance,so it can store data records and other important information on the blockchain,but the original medical data is stored in Distributed database.The use’s medical health data is stored securely,meanwhile the sharing efficiency among the medical institutions is improved.Security analysis shows that the proposed model can protect medical health data within the scope of fault tolerance,prevent the data from tampering and the collusion problem.The proposed model has a 99%probability to ensure that the medical institutions can reach a consensus and share medical data in alliance by the consistency analysis.