Airavat: security and privacy for MapReduce
Indrajit Roy, Srinath Setty, Ann Kilzer, Vitaly Shmatikov, Emmett Witchel · 2010
The cloud computing paradigm, which involves distributed computation on multiple large-scale datasets, will become successful only if it ensures privacy, confidentiality, and integrity for the data belonging to individuals and organizations. We present Airavat, a novel integration of decentralized information flow control (DIFC) and differential privacy that provides strong security and privacy guarantees for MapReduce computations. Airavat allows users to use arbitrary mappers, prevents unauthorized leakage of sensitive data during the computation, and supports automatic declassification of the results when the latter do not violate individual privacy. Airavat minimizes the amount of trusted code in the system and allows users without security expertise to perform privacy-preserving computations on sensitive data. Our prototype implementation demonstrates the flexibility of Airavat on a wide variety of case studies. The prototype is efficient, with run-times on Amazon’s cloud computing infrastructure within 25 % of a MapReduce system with no security.