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Opaque: an oblivious and encrypted distributed analytics platform

TLDR
The proposed Opaque introduces new distributed oblivious relational operators that hide access patterns, and new query planning techniques to optimize these new operators to improve performance.
Abstract
Many systems run rich analytics on sensitive data in the cloud, but are prone to data breaches. Hardware enclaves promise data confidentiality and secure execution of arbitrary computation, yet still suffer from access pattern leakage. We propose Opaque, a distributed data analytics platform supporting a wide range of queries while providing strong security guarantees. Opaque introduces new distributed oblivious relational operators that hide access patterns, and new query planning techniques to optimize these new operators. Opaque is implemented on Spark SQL with few changes to the underlying system. Opaque provides data encryption, authentication and computation verification with a performance ranging from 52% faster to 3.3x slower as compared to vanilla Spark SQL; obliviousness comes with a 1.6-46x overhead. Opaque provides an improvement of three orders of magnitude over state-of-the-art oblivious protocols, and our query optimization techniques improve performance by 2-5x.

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Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution

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References
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Proceedings ArticleDOI

Fully homomorphic encryption using ideal lattices

TL;DR: This work proposes a fully homomorphic encryption scheme that allows one to evaluate circuits over encrypted data without being able to decrypt, and describes a public key encryption scheme using ideal lattices that is almost bootstrappable.
Proceedings Article

Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing

TL;DR: Resilient Distributed Datasets is presented, a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner and is implemented in a system called Spark, which is evaluated through a variety of user applications and benchmarks.
Journal ArticleDOI

Software protection and simulation on oblivious RAMs

TL;DR: This paper shows how to do an on-line simulation of an arbitrary RAM by a probabilistic oblivious RAM with a polylogaithmic slowdown in the running time, and shows that a logarithmic slowdown is a lower bound.
Journal Article

MLlib: machine learning in apache spark

TL;DR: MLlib as mentioned in this paper is an open-source distributed machine learning library for Apache Spark that provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives.
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