A risk adaptive access control model based on Markov for big data in the cloud
by Hongfa Ding; Changgen Peng; Youliang Tian; Shuwen Xiang
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 13, No. 4, 2019

Abstract: The main problems of the application of access control in the cloud are the necessary flexibility and scalability to support a large number of users and resources in a dynamic and heterogeneous environment, with collaboration and information sharing needs. This paper proposes a risk self-adaptive dynamic access control model, based on Markov chain and Shannon information theory, for big data that stored and processed by cloud. In this model, a simple formal adversary model, a modification of XACML framework including some new and enhanced components, Markov-based methods for calculating the risk values of access requests, and an incentive mechanism for supervising all the access behaviours of subjects are proposed, successively. Our method is easy to deploy and the administrator just need to label the object data. This method is more effective and suitable to control the access in large-scale information system, and protect the sensitive and privacy data.

Online publication date: Wed, 24-Apr-2019

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