云平台下基于属性加密的高铁共享数据访问控制及应急决策方法研究

发布时间：2018-02-25 08:39

本文关键词： Hadoop 高速铁路属性加密知识约简贝叶斯网络　出处：《北京交通大学》2017年硕士论文　论文类型：学位论文

【摘要】：云计算技术在大数据管理方面的优势使其成为最受欢迎的主流计算方式,因此存储大规模数据时的最佳选择是利用云存储技术进行,而云存储中最关键的问题就是安全问题。本文根据云平台下高铁信息共享技术的相关研究以及共享数据安全性要求,主要进行了如下研究:首先,对云平台下高速铁路共享数据存储的安全需求进行了分析,针对不同密级的数据提出了不同的安全策略,重点研究秘密级数据的加密及访问控制方法。文中对现有的数据加密方法进行了分析,包括基于属性加密技术的算法流程、优缺点及其在高铁数据加密应用中存在的问题。其次,对文中使用的基本理论包括云计算技术、属性加密方法及粗糙集理论的基础知识进行了介绍,同时提出了基于属性加密算法访问控制技术的改进方案。通过直接构造共享生成矩阵的方式改变了访问结构的表示方法,同时,本文引入了属性组的概念,实现了用户在属性层面的权限撤销功能。再次,分析了高速铁路突发事件应急场景,建立了应急情景的分层网络模型。提出了基于情景分析的并行贝叶斯网络约简模型。搭建了Hadoop云平台,通过设计map函数、reduce函数、call-job函数以及主程序实现了基于粗糙集理论的知识约简算法的并行运算。利用贝叶斯网络对约简后的结果进行决策推理,并使用Netica贝叶斯仿真软件对模型进行了验证。最后,本文设计了加密和解密算法的应用系统,并实现了部分功能。测试了云存储条件下的高铁数据加密算法的时间性能,并对算法的时间开销和空间开销进行了分析。
[Abstract]:The advantages of cloud computing technology in big data's management make it the most popular mainstream computing method, so the best choice for storing large-scale data is to use cloud storage technology. The most critical problem in cloud storage is security. According to the related research of high-speed railway information sharing technology under cloud platform and the security requirements of shared data, this paper mainly carries out the following research: first, The security requirements of shared data storage for high-speed railway under cloud platform are analyzed, and different security strategies are proposed for different data classes. This paper focuses on the encryption and access control methods of secret level data, and analyzes the existing data encryption methods, including the algorithm flow based on attribute encryption technology. The advantages and disadvantages and the problems existing in the application of high-speed railway data encryption are introduced. Secondly, the basic theories used in this paper include cloud computing technology, attribute encryption method and the basic knowledge of rough set theory. At the same time, an improved access control scheme based on attribute encryption algorithm is proposed. The representation method of access structure is changed by constructing a shared generation matrix directly. At the same time, the concept of attribute group is introduced in this paper. The function of user authority revocation at attribute level is realized. Thirdly, the emergency scene of high-speed railway emergency is analyzed. A hierarchical network model of emergency scenarios is established, and a parallel Bayesian network reduction model based on scenario analysis is proposed. The Hadoop cloud platform is built. The parallel operation of knowledge reduction algorithm based on rough set theory is realized by designing the map reduce function call-job function and the main program. The Bayesian network is used to infer the result of reduction. Finally, the application system of encryption and decryption algorithm is designed, and some functions are realized. The time performance of high-speed iron data encryption algorithm under cloud storage is tested. The time and space cost of the algorithm are analyzed.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP309;U29-39

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