• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 高性能计算 • 上一篇    下一篇

基于有限域代数签名分治表的大数据云存储远程审计

钱政1,2,夏红霞2   

  1. (1.合肥工业大学计算机与信息学院,安徽 合肥 233009;2.安徽电子信息职业技术学院,安徽 蚌埠 233030)
  • 收稿日期:2018-04-23 修回日期:2018-05-29 出版日期:2018-11-25 发布日期:2018-11-25
  • 基金资助:

    安徽高校自然科学研究项目(KJ2018A0780);安徽省大学生创容实验室建设计划项目(2016ckjh019)

Remote audit of big data cloud storage based on
 finite field algebraic signature partition table

QIAN Zheng1,2,XIA Hongxia2   

  1. (1.School of Computer and Information,Hefei University of Technology,Hefei 233009;
    2.Department of Information and Intelligence Engineering,
    Anhui Vocational College of Electronics & Information Technology,Bengbu 233030,China)

     
  • Received:2018-04-23 Revised:2018-05-29 Online:2018-11-25 Published:2018-11-25

摘要:

为提高大数据存储过程的审计效率,提出基于有限域代数签名分治表远程数据检查RDC的云计算大数据存储审计方法。首先,通过使用外包文件的代数签名,利用底层字段算术运算完成云存储中数据完整性的远程检测,所提数据审计方法对客户端和云服务端具有相对较低的计算和通信成本。其次,设计了分治表D&CT作为一种新的数据结构,以有效地支持动态数据操作,如插入、追加、删除和修改操作。采用D&CT方法可令所提RDC方案适用于各种大小的文件云存储过程分析。最后,通过仿真实验,验证了所提方法在大数据云存储过程中的有效性。
 

关键词: 分治表, 大数据, 云存储, 远程数据检查, 数据审计

Abstract:

In order to improve the audit efficiency of big data storage, we propose a big data storage audit method based on the remote data checking (RDC) of the finite field algebraic signature partition table. Firstly, after obtaining the algebraic signature of outsourced files, we use the arithmetic operation of the underlying field to complete the remote detection of data integrity in the cloud storage. The proposed data audit method has relatively low computation and communication costs for both the client and the cloud server. Secondly, we design a divide and conquer table (D&CTs) as a new data structure to effectively support dynamic data operations such as insert, add, delete, and modify operations. The D&CTs method can enable the proposed RDC scheme to be applied to cloud storage process analysis with varied size of files. Finally, simulation experiments are carried out to verify the effectiveness of the proposed method in big data cloud storage process.
 

Key words: divide and conquer table, big data, cloud storage, remote data check, data audit