Opaque: An Oblivious and Encrypted Distributed Analytics Platform

By Wenting Zheng, Ankur Dave, Jethro Gideon Beekman, Raluca Ada Popa, Joseph E. Gonzalez, Ion Stoica. 2017

404 citations recorded by the source corpus.

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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.”
A Pragmatic Introduction to Secure Multi-Party Computation (2019, background): “Another approach that can produce dramatic performance improvements is employing secure hardware such as Intel SGX or secure smartcards (Ohrimenko et al., 2016; Gupta et al., 2016; Bahmani et al., 2017; Zheng et al., 2017).”
CrypTFlow: Secure TensorFlow Inference (2019, methodology): “Prior works [74], [69], [38], [11], [23], [29], [51], [32], [77], [83], [44] that combine MPC and hardware (SGX) make stronger trust assumption on the hardware of both confidentiality and integrity, and hence, provide security only in a weaker threat model where the hardware hides the data residing ”
An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors (2018, background): “Related work There are several systems that propose conﬁdential data analysis using TEEs [37, 54, 46, 8].”
Untangling Blockchain: A Data Processing View of Blockchain Systems (2017, methodology): “Recent distributed systems are leveraging trusted hardware such as Intel SGX and ARM TrustZone to improve performance at a slight cost of security [49], [50].”
PROCHLO: Strong Privacy for Analytics in the Crowd (2017, methodology): “Since this approach requires oblivious sorting anyway, it can be combined with the shuffling operation itself, obviating the need for something more efficient like the Stash Shuffler; in fact, this is a specialized form of moregeneral oblivious relational operators, such as those proposed in Opaque ”
SgxPectre: Stealing Intel Secrets from SGX Enclaves Via Speculative Execution (2018, background): “y guarantees. Although SGX is still in its infancy, the promise of shielded execution has encouraged researchers and practitioners to develop various new applications to utilize these features (e.g., [2, 22, 51, 54, 59, 68, 69, 85, 88]), and new software tools or frameworks (e.g., [4, 5, 9, 24, 43, ”
Serverless Computing: One Step Forward, Two Steps Back (2018, background): “Technologies like hardware enclaves can help protect running code, and there has been initial work on data processing in those settings (e.g., [28]).”
YerbaBuena: Securing Deep Learning Inference Data via Enclave-based Ternary Model Partitioning (2018, unknown): “Opaque[56] was a distributed data analytics platform introducing SGX-enabled oblivious relational operators to mask data access patterns.”
SgxPectre Attacks: Leaking Enclave Secrets via Speculative Execution (2018, background): “y guarantees. Although SGX is still in its infancy, the promise of shielded execution has encouraged researchers and practitioners to develop various new applications to utilize these features (e.g., [2, 22, 51, 54, 59, 68, 69, 85, 88]), and new software tools or frameworks (e.g., [4, 5, 9, 24, 43, ”
Graphene-SGX: A Practical Library OS for Unmodified Applications on SGX (2017, methodology): “Opaque [57] uses an SGX-protected layer on the Spark framework to generate oblivious relational operators that hide the access patterns of distributed queries.”
Chiron: Privacy-preserving Machine Learning as a Service (2018, background): “posed systems aim to protect applications from an untrusted platform. Haven [24], SCONE [22], and GrapheneSGX [29] provide an environment to support unmodiﬁed legacy applications. VC3 [52] and Opaque [68] provide SGXprotected data processing platforms. All of these systems assume that all of the cod”