Abstract
The MapReduce framework has been widely adopted for processing Big Data in the cloud. While efficient, MapReduce offers very complicated (if any) means for users to request nodes that satisfy certain security and privacy requirements to process their data.
In this paper, we propose a novel approach to seamlessly integrate node selection control to the MapReduce framework for increasing data security. We define a succinct yet expressive policy language for MapReduce environments, according to which users can specify their security and privacy concerns over their data. Then, we propose corresponding data preprocessing techniques and node verification protocols to achieve strong policy enforcement. Our experimental study demonstrates that, compared to the traditional MapReduce framework, our policy control mechanism allows to achieve data privacy without introducing significant overhead.
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Acknowledgement
Portion of the work from Dr. Squicciarini was funded under the auspices of National Science Foundation, Grant #1250319. Portion of the work from Dan Lin was funded by the National Science Foundation (NSF-CNS-1250327 and NSF-DGE-1433659).
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Squicciarini, A.C., Lin, D., Sundareswaran, S., Li, J. (2015). Policy Driven Node Selection in MapReduce. In: Tian, J., Jing, J., Srivatsa, M. (eds) International Conference on Security and Privacy in Communication Networks. SecureComm 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-319-23829-6_5
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