Home > Archive > 2015 > Volume 5 Number 3 (Jun. 2015) >
IJMLC 2015 Vol. 5(3): 230-234 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.512

Protecting Cloud Data Using the Decentralized Information Flow Control with Authorization Condition

Ye Jianwei, Xu Jie, Jiao Xulu, and Xu Zhikai

Abstract—In the extremely dynamic cloud computing system, traditional access control technologies provide no autonomic authorization and access control for the users on their data in remote cloud. Once data is migrated to the cloud, the user transfers the control to the providers of the cloud services and cloud hardware. So, whether the data is proper protected will be the users’ most primary concerns and major challenges. This paper proposes a new decentralized information flow control model- DIFC-AC and its implementation. It expands the security label of DIFC with authorization condition used to express the control demands of the user, and access to the data is arbitrated based on their labels by intercepting IPC-relevant system calls. Thereby, the controls on the data are reached to the cloud, and sequentially the users’ demands on the confidentiality, integrity and controllability of their data are meet.

Index Terms—Access control, authenticity, cloud computing, confidentiality, decentralized information flow control.

Ye Jianwei is with Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China (e-mail: yejianwei@iie.ac.cn).
Xu Jie is with National Computer Network Emergency Response Technical Team Coordination Center of China, China (e-mail: xujie@cert.org.cn).
Jiao Xulu is with Information Center of Ministry of Industry and Information Technology of China, China (e-mail: jxl@miit.gov.cn).
Xu Zhikai is with Haerbin Institute of Technology, Heilongjiang, China (e-mail: zhikaixu@foxmail.com).

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Cite: Ye Jianwei, Xu Jie, Jiao Xulu, and Xu Zhikai, "Protecting Cloud Data Using the Decentralized Information Flow Control with Authorization Condition," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 230-234, 2015.

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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