DocumentCode
249304
Title
Vigiles: Fine-Grained Access Control for MapReduce Systems
Author
Ulusoy, Huseyin ; Kantarcioglu, Murat ; Pattuk, Erman ; Hamlen, Kevin
Author_Institution
Univ. of Texas at Dallas, Richardson, TX, USA
fYear
2014
fDate
June 27 2014-July 2 2014
Firstpage
40
Lastpage
47
Abstract
Security concerns surrounding the rise of Big Data systems have stimulated myriad new Big Data security models and implementations over the past few years. A significant disadvantage shared by most of these implementations is that they customize the underlying system source code to enforce new policies, making the customizations difficult to maintain as these layers evolve over time (e.g., over version updates). This paper demonstrates how a broad class of safety policies, including fine-grained access control policies at the level of key-value data pairs rather than files, can be elegantly enforced on MapReduce clouds with minimal overhead and without any change to the system or OS implementations. The approach realizes policy enforcement as a middleware layer that rewrites the cloud´s front-end API with reference monitors. After rewriting, the jobs run on input data authorized by fine-grained access control policies, allowing them to be safely executed without additional system-level controls. Detailed empirical studies show that this more modular approach exhibits just 1% overhead compared to a less modular implementation that customizes MapReduce directly to enforce the same policies.
Keywords
Big Data; authorisation; cloud computing; middleware; Big Data; MapReduce clouds; MapReduce systems; Vigiles; fine-grained access control policies; front-end API; key-value data pairs; middleware layer; reference monitors; Access control; Big data; Computational modeling; Data models; Java; Programming; Access Control; MapReduce; Security;
fLanguage
English
Publisher
ieee
Conference_Titel
Big Data (BigData Congress), 2014 IEEE International Congress on
Conference_Location
Anchorage, AK
Print_ISBN
978-1-4799-5056-0
Type
conf
DOI
10.1109/BigData.Congress.2014.16
Filename
6906759
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