A Security Protection Framework for Cloud Computing


Wenzheng Zhu, Changhoon Lee, Journal of Information Processing Systems Vol. 12, No. 3, pp. 538-547, Jun. 2016  

10.3745/JIPS.03.0053
Keywords: Cloud computing, Collusive Worker, Malicious Worker, MapReduce, Non-collusive Worker
Fulltext:

Abstract

Cloud computing is a new style of computing in which dynamically scalable and reconfigurable resources are provided as a service over the internet. The MapReduce framework is currently the most dominant programming model in cloud computing. It is necessary to protect the integrity of MapReduce data processing services. Malicious workers, who can be divided into collusive workers and non-collusive workers, try to generate bad results in order to attack the cloud computing. So, figuring out how to efficiently detect the malicious workers has been very important, as existing solutions are not effective enough in defeating malicious behavior. In this paper, we propose a security protection framework to detect the malicious workers and ensure computation integrity in the map phase of MapReduce. Our simulation results show that our proposed security protection framework can efficiently detect both collusive and non-collusive workers and guarantee high computation accuracy.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Wenzheng Zhu and Changhoon Lee (2016). A Security Protection Framework for Cloud Computing. Journal of Information Processing Systems, 12(3), 538-547. DOI: 10.3745/JIPS.03.0053.

[IEEE Style]
W. Zhu and C. Lee, "A Security Protection Framework for Cloud Computing," Journal of Information Processing Systems, vol. 12, no. 3, pp. 538-547, 2016. DOI: 10.3745/JIPS.03.0053.

[ACM Style]
Wenzheng Zhu and Changhoon Lee. 2016. A Security Protection Framework for Cloud Computing. Journal of Information Processing Systems, 12, 3, (2016), 538-547. DOI: 10.3745/JIPS.03.0053.