• 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