Airavat: security and privacy for MapReduce

  • Authors:

  • Indrajit Roy;Srinath T. V. Setty;Ann Kilzer;Vitaly Shmatikov;Emmett Witchel

  • Affiliations:

  • The University of Texas at Austin;The University of Texas at Austin;The University of Texas at Austin;The University of Texas at Austin;The University of Texas at Austin

  • Venue:
  • NSDI'10 Proceedings of the 7th USENIX conference on Networked systems design and implementation
  • Year:
  • 2010

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Abstract

We present Airavat, a MapReduce-based system which provides strong security and privacy guarantees for distributed computations on sensitive data. Airavat is a novel integration of mandatory access control and differential privacy. Data providers control the security policy for their sensitive data, including a mathematical bound on potential privacy violations. Users without security expertise can perform computations on the data, but Airavat confines these computations, preventing information leakage beyond the data provider's policy. Our prototype implementation demonstrates the flexibility of Airavat on several case studies. The prototype is efficient, with run times on Amazon's cloud computing infrastructure within 32% of a MapReduce system with no security.