Airavat: Security and Privacy for MapReduce

By Indrajit Roy, Srinath T. V. Setty, Ann Kilzer, Vitaly Shmatikov, Emmett Witchel. 2010

558 citations recorded by the source corpus.

Subfields: ML Systems & Hardware, Privacy & Security

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In Privacy & Security

Citations in the corpus

Detecting Violations of Differential Privacy (2018, methodology): “Programming platforms, such as PINQ [29], Airavat [37], and GUPT [32] provide a small set of primitive operations that can be used as building blocks of algorithms for differential privacy.”
Prochlo: Strong Privacy for Analytics in the Crowd (2017, methodology): “The analyzer, which has the most complete view of the reported data, may choose from a wide variety of differentially private mechanisms and data processing systems [27,31,38, 53, 63].”
Advanced Probabilistic Couplings for Differential Privacy (2016, neutral): “PINQ [36] internalizes the use of standard composition in the form of a privacy budget management platform, Airavat [45] uses differential privacy combined with the map-reduce approach, GUPT [40] implements the general idea of sample and aggregate [41].”
VC3: Trustworthy Data Analytics in the Cloud Using SGX (2015, background): “Airavat [52] defends against possibly malicious map function implementations using differential privacy.”
Proving Differential Privacy in Hoare Logic (2014, background): “On a more practical side, it would be interesting to implement our transformation for a realistic setting, for instance modeling thePINQ language [30].”
Towards Practical Differential Privacy for SQL Queries (2017, background): “Airavat [48] enforces differential privacy for arbitrary MapReduce programs, but requires the analyst to bound the range of possible outputs of the program, and clamps output values to lie within that range.”
AutoPriv: Automating Differential Privacy Proofs (2016, background): “The first is the use of programming platforms (McSherry 2009; Mohan et al. 2012; Roy et al. 2010) that have privacy primitives that restrict the privacy-preserving algorithms that can be implemented and often add more noise than is necessary to the computation .”
Testing the Lipschitz Property over Product Distributions with Applications to Data Privacy (2013, background): “One approach (e.g. [25,30,29]) that has been taken to address the above problem is to require clients to write their programs using a speciﬁc set of trusted built-in functions provided by the platform.”
Security issues in cloud environments: a survey (2014, background): “Moreover, MapReduce does not have an integrated security model because it was designed to run in a single data center [234].”
Towards Statistical Queries over Distributed Private User Data (2012, background): “In spite of the fact that an increasing number of queries can lead to increased privacy loss, we find differential privacy to be an attractive model both because it does provide measurable privacy, and because there is substantial ongoing effort in developing its uses [32, 38, 39, 40, 47] and unders”
Privacy-preserving deep learning (2015, methodology): “A similar technique was previously used to enforce privacy of MapReduce computations with untrusted mappers [40].”
PRISM - Privacy-Preserving Search in MapReduce (2012, background): “[41] propose a security solution dedicated to MapReduce.”
Probabilistic relational reasoning for differential privacy (2012, methodology): “SQL-like languages [24], higher-order functional languages [29], imperative languages [9], the MapReduce framework [30], and I/O automata [35].”
Observing and Preventing Leakage in MapReduce (2015, background): “Airavat [23] defends against possibly malicious map function implementations using differential privacy.”
M2R: Enabling Stronger Privacy in MapReduce Computation (2015, unknown): “using differential privacy [48]).”