roy - Airavat Security and Privacy for MapReduce Indrajit...

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Airavat: Security and Privacy for MapReduce Indrajit Roy Srinath T.V. Setty Ann Kilzer Vitaly Shmatikov Emmett Witchel The University of Texas at Austin { indrajit, srinath, akilzer, shmat, witchel } @cs.utexas.edu Abstract We present Airavat, a MapReduce-based system which provides strong security and privacy guarantees for dis- tributed computations on sensitive data. Airavat is a novel integration of mandatory access control and differ- ential 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 Aira- vat confines these computations, preventing information leakage beyond the data provider’s policy. Our prototype implementation demonstrates the flexi- bility 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. 1 Introduction Cloud computing involves large-scale, distributed com- putations on data from multiple sources. The promise of cloud computing is based in part on its envisioned ubiq- uity: Internet users will contribute their individual data and obtain useful services from the cloud. For example, targeted advertisements can be created by mining a user’s clickstream, while health-care applications of the future may use an individual’s DNA sequence to tailor drugs and personalized medical treatments. Cloud computing will fulfill this vision only if it supports flexible compu- tations while guaranteeing security and privacy for the in- put data. To balance the competing goals of a permissive programming model and the need to prevent information leaks, the untrusted code should be confined [30]. Contributors of data to cloud-based computations face several threats to their privacy. For example, consider a medical patient who is deciding whether to participate in a large health-care study. First, she may be concerned that a careless or malicious application operating on her data as part of the study may expose it—for instance, by writing it into a world-readable file which will then be indexed by a search engine. Second, she may be con- cerned that even if all computations are done correctly and securely, the result itself, e.g. , aggregate health-care statistics computed as part of the study, may leak sensi- tive information about her personal medical record. Traditional approaches to data privacy are based on syntactic anonymization, i.e. , removal of “personally identifiable information” such as names, addresses, and Social Security numbers. Unfortunately, anonymiza- tion does not provide meaningful privacy guarantees. High-visibility privacy fiascoes recently resulted from public releases of anonymized individual data, includ- ing AOL search logs [22] and the movie-rating records of Netflix subscribers [41]. The datasets in question were released to support legitimate data-mining and collaborative-filtering research, but na¨
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