Observing and Preventing Leakage in MapReduce

By O. Ohrimenko, Manuel Costa, Cédric Fournet, Christos Gkantsidis, Markulf Kohlweiss, Divya Sharma. 2015

92 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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Citations in the corpus

Prochlo: Strong Privacy for Analytics in the Crowd (2017, methodology): “In particular, for the types of data center analytics that would be performed by the ESA analyzer, there have been several proposals in the recent literature [20, 57, 67].”
Oblivious Multi-Party Machine Learning on Trusted Processors (2016, methodology): “, MapReduce [47, 49]).”
Opaque: An Oblivious and Encrypted Distributed Analytics Platform (2017, background): “Unfortunately, enclaves still suffer from an important attack vector: access pattern leakage [41, 28].”
PROCHLO: Strong Privacy for Analytics in the Crowd (2017, methodology): “In particular, for the types of data center analytics that would be performed by the ESA analyzer, there have been several proposals in the recent literature [20, 57, 67].”
UnLynx: A Decentralized System for Privacy-Conscious Data Sharing (2017, background): “many scenarios [29, 37, 47], which shows the immaturity of deploying such systems - at their current stage - to address critical challenges such as secure data sharing.”
CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel (2019, methodology): “used a secure implementation of MapReduce jobs and analyzed intermediate traffic between reducers and mappers [37].”
Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute (2017, methodology): “Recent systems [20, 37] adopt a similar approach to this paper’s to support privacy-preserving computation.”
SGXBOUNDS: Memory Safety for Shielded Execution (2017, background): “We must note that even though we concentrate on memory safety, there are other, insider attack vectors (orthogonal to our work) where a malicious OS tries to deceive the shielded application [30, 60, 71, 83].”
HOP: Hardware makes Obfuscation Practical (2017, background): “An adversary observing communication patterns between a processor and its memory can still infer significant information about the data [43], [58].”
Sealed-Glass Proofs: Using Transparent Enclaves to Prove and Sell Knowledge (2017, background): “The question immediately arises: Is this weaker model of trusted hardware useful? Many prior works have proposed using trusted hardware for secure outsourcing of computations, including protection of cryptographic keys (Flicker) [18], Digital Rights Management (Aegis) [16], Map-Reduce jobs (M(2)R) [”