基于信用等级划分的医疗数据安全共识算法

陈友荣; 陈浩; 韩蒙; 刘半藤; 王章权; 任条娟

doi:10.11999/JEIT200893

基于信用等级划分的医疗数据安全共识算法

doi: 10.11999/JEIT200893

陈友荣^{1, 3},
陈浩²,
韩蒙^{3, 4, ,},
刘半藤¹,
王章权¹,
任条娟¹

1.
浙江树人大学信息科技学院杭州 310015
2.
常州大学计算机与人工智能学院常州 213164
3.
浙江大学计算机科学与技术学院杭州 310013
4.
美国肯尼索州立大学计算与软件工程学院玛丽埃塔 30060

基金项目: 浙江省科技厅“领雁”研发攻关计划资助项目(2022C03122)，浙江省公益性技术应用研究计划项目(LGG19F010011, LGF19F010005)

详细信息

作者简介:
陈友荣：男，1982年生，博士，教授，主要研究方向为网络安全和物联网应用

陈浩：男，1998年生，硕士生，研究方向为区块链

韩蒙：男，1987年生，博士，研究员，主要研究方向为数据驱动的安全和区块链

刘半藤：男，1984年生，博士，副教授，主要研究方向为网络安全和人工智能

王章权：男，1969年生，博士，教授，主要研究方向为无线传感网

任条娟：女，1965年生，博士，教授，主要研究方向为网络安全和车联网

通讯作者:
韩蒙　mhan@zju.edu.cn

中图分类号: TN915.08; TP309
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- 被引次数: 0
出版历程
- 收稿日期: 2020-10-19
- 修回日期: 2021-05-27
- 网络出版日期: 2021-07-15
- 刊出日期: 2022-01-10

Security Consensus Algorithm of Medical Data Based on Credit Rating

1.
College of Information Science and Technology, Zhejiang Shuren University, Hangzhou 310015, China
2.
School of Computer and Artificial Intelligence, Changzhou University, Changzhou 213164, China
3.
College of Computer Science and Technology, Zhejiang University, Hangzhou 310013, China
4.
College of Computing and Software Engineering, Kennesaw State University, Marietta 30060, USA

Funds: The “Ling Yan” Research and Development Project of Science and Technology Department of Zhejiang Province (2022C03122), The Public Welfare Technology Application and Research Projects of Zhejiang Province of China (LGG19F010011, LGF19F010005)

摘要

摘要: 在基于区块链的医疗数据共享系统中为防御恶意节点攻击并且提高共识效率，该文提出基于信用等级划分的医疗数据安全共识算法(SCA_MD)。首先，在SCA_MD中，考虑由数据节点、共识节点和监管节点组成的医疗区块共识模型，提出相应的节点身份验证机制，实现快速验证。其次，提出基于海洋掠食者的自我优化信用等级划分算法(SCRD)，以限制恶意节点共识权力。最后改进代表节点选举机制和共识机制，提高共识的效率。实验结果表明：不管恶意节点数量如何变化，SCA_MD都能提高交易吞吐量，降低平均交易时延和平均节点通信开销。
- 区块链 /
- 医疗数据 /
- 安全共识 /
- 信用等级
Abstract: In order to defend against malicious node attacks and improve consensus efficiency in the blockchain-based medical data sharing system, a Security Consensus Algorithm of Medical Data (SCA_MD) based on credit rating is proposed. In SCA_MD, the medical block consensus model which consists of data-based nodes, consensus nodes and supervisory nodes is considered. The corresponding node identity verification mechanism is proposed to achieve rapid verification. Then, a Self-optimizing Credit Rating Division (SCRD) algorithm based on marine predators is proposed to limit the consensus power of malicious nodes. Finally, the voting mechanism and consensus mechanism of representative nodes are proposed to improve the efficiency of consensus. The experimental results show that no matter how the number of malicious nodes changes, SCA_MD can increase transaction throughput, and reduce average transaction delay and average node communication overhead.
- Bockchain /
- Medical data /
- Security consensus /
- Cedit grade

HTML全文

图 1 医疗区块共识模型

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图 2 SCA_MD共识算法原理

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图 3 信任值模型参数对交易吞吐量的影响

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图 4 恶意节点数量对查全率与查准率的影响

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图 5 恶意节点数量对交易吞吐量的影响

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图 6 恶意节点数量对平均交易时延的影响

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图 7 恶意节点数量对平均节点通信开销的影响

下载: 全尺寸图片幻灯片

表 1 基于信用等级划分的医疗数据安全共识算法 (SCA_MD)

输入: 网络中节点的基本信息
输出: 网络对医疗数据的区块共识结果
(1) $\eta $=80; $\lambda $=80; $\mu $=20; $\gamma $=180; $\sigma $=180; $\theta $=350; ···;
(2) 本节点将自身投票信息广播给其他节点;
(3) 如果本节点收到全部节点的投票信息然后
(4) 　本节点统计投票结果，选择${\rm{NAN}}$个代表节点，并广播其节　　　点信息;
(5) end
(6) while 1
(7) 　如果本节点成为代表节点然后
(8) 　　本节点分别对$c$个节点执行节点身份验证机制;
(9) 　　如果当前为第一次共识then
(10) 　　　本节点采用改进的PBFT共识算法完成区块共识;
(11) 　　否则
(12) 　　　本节点分别计算$\theta $个节点的累积信用值$y(t)$，并采集　　　　　　其评估要素${y_l}$, ${\nu _l}$, ${\upsilon _l}$和${\chi _l}$;
(13) 　　　如果网络需要重新进行节点等级划分然后
(14) 　　　　本节点执行信用等级划分算法SCRD，并根据等级　　　　　　　结果给予每一个节点相应权力;
(15) 　　　end
(16) 　　　如果网络需要重新进行代表节点选择然后
(17) 　　　　本节点根据公式(13)分别计算$b$个节点的得分，并　　　　　　　广播自身投票信息与候选节点信息;
(18) 　　　end
(19) 　　　如果本节点收到全部节点的投票信息然后
(20) 　　　　本节点根据式(14)分别计算$e$个节点的投票结果，　　　　　　　选择${\rm{NAN}}$个代表节点，并广播;
(21) 　　　end
(22) 　　　本节点采用改进一致性协议和视图切换协议完成区　　　　　　块共识;
(23) 　　end
(24) 　else
(25) 　　本节点从网络中同步区块信息，并根据网络要求进行　　　　　投票;
(26) 　end
(27) end

下载: 导出CSV

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