M2R: Enabling Stronger Privacy in MapReduce Computation

By Tien Tuan Anh Dinh, Prateek Saxena, Ee-Chien Chang, Beng Chin Ooi, Chunwang Zhang. 2015

131 citations recorded by the source corpus.

Subfields: ML Systems & Hardware, Privacy & Security

How researchers see this paper

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

In ML Systems & Hardware

In Privacy & Security

Citations in the corpus

Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts (2018, neutral): “M2R [28], VC3 [77], Opaque [94] and Ohrimenko et al.”
Prochlo: Strong Privacy for Analytics in the Crowd (2017, background): “dozen million items, at most, even if we ignore storage space for actual data, computation, etc. Finally, instead of sorting, cascade-mix networks have been proposed for oblivious shufﬂing (e.g., M2R [23]). With SGX, such networks split the input data across SGX enclaves, partitioned to ﬁt in SGX pr”
Machine Learning Models that Remember Too Much (2017, background): “ed in [67]. Abadi et al. [1] describe how to train differentially private deep learning models. Systems using trusted hardware such as SGX protect training data while training on an untrusted service [21, 61, 66]. In all of these works, the training algorithm is public and agreed upon, and our attac”
Inferring Fine-grained Control Flow Inside SGX Enclaves with Branch Shadowing (2016, unknown): “Several security applications of SGX are proposed, including secure and distributed data analysis [7, 11, 39, 46, 66] and secure networking service [31, 41, 48].”
A Vademecum on Blockchain Technologies: When, Which, and How (2019, background): “In particular, Endorsement key pair (EK) used for encryption, never visible outside trusted platform modules, is burnt into each device during manufacturing [173].”
Preventing Page Faults from Telling Your Secrets (2016, background): “These attacks may apply to cloud server platforms [10, 15, 21, 22, 41, 49, 54, 55].”
Formal Abstractions for Attested Execution Secure Processors (2017, background): “Potential applications are numerous, ranging from cloud computing [11, 28, 50, 61, 62], mobile security [60], web security, to cryptocurrencies [74].”
PROCHLO: Strong Privacy for Analytics in the Crowd (2017, background): “Therefore, oblivious shuffling has received significant attention as interest in trustworthy hardware has rekindled [20, 23, 57, 78].”
Opaque: An Oblivious and Encrypted Distributed Analytics Platform (2017, background): “[28] and M2R [10] provide mechanisms for reducing network traffic analysis leakage for MapReduce jobs.”
Sealed-Glass Proofs: Using Transparent Enclaves to Prove and Sell Knowledge (2017, methodology): “applications [6] as well as Map-Reduce computations [22].”
Privacy-Preserving Computation with Trusted Computing via Scramble-then-Compute (2017, methodology): “Unfortunately, such a channel could leak information about the data [17, 20, 45].”