基于医疗健康大数据的安全起源模型与可信性验证算法  

Securing data provenance and creditability validation study based on big data of health care

作　　者：王凤英[1] 张方[1] 张伟[1] 

机构地区：[1]山东理工大学计算机科学与技术学院,山东淄博255049

出　　处：《山东理工大学学报（自然科学版）》2017年第6期6-11,共6页Journal of Shandong University of Technology:Natural Science Edition

基　　金：国家自然科学基金项目(61473179);山东省重点研发计划项目(2016GGX101027);山东省自然科学基金项目(ZR2014FM007;ZR2013FM013)

摘　　要：面对医疗健康大数据,使用者或决策者难以判定其来源及是否可信.为了得到可信的数据,需要知道它的安全起源,同时确保数据起源的安全.针对上述问题,提出了基于W3CPROV的安全数据起源模型PROV-S,研究安全起源关系图中的各种标注对象,定义了安全起源伴生节点、安全伴生关系、触发关系以及各组件之间的关联关系.在安全模型PROV-S的基础上,以安全关系类的完整性子类为例,提出了一级完整性设计和验证方案,给出了具有实现可信性的完整性验证方案,并分析了其特点.通过对模型与可信性方案的安全及效率分析表明,建立的模型能保证医疗健康数据的安全性,完整性验证方案能保证数据来源的可信性.In big data of health care environment, it is difficult for users and decision makers to determine if data provenance is trustworthy. In order to obtain reliable data, we need to know its secure provenance, so ensuring the security of the data provehance is of great significance to information security. To solve these problems, we propose securing data provenance model PROVS, which studies the various label objects in securing provenance relationship diagram and defines concomitancy nodes of securing provenance, secure concomitancy relationships, trigger relationships, and association relationships between components. Based on the PROV-S, using the com- pleteness subclass of security relationship class as an example, we propose a creditability scheme of first-level provenance completeness, give an algorithm for complete creditability verification, and analyze the algorithm＇s characteristics. Finally, analysis to the safety and efficiency of models and credible scheme shows that the established model could ensure security of health care data, and integrity verification scheme could ensure the credibility of data provenance.

关 键 词：医疗健康大数据 安全起源模型 可信性验证 安全伴生关系 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]