FairplayMP: a system for secure multi-party computation

By Assaf Ben-David, Noam Nisan, Benny Pinkas. 2008

593 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

Prio: Private, Robust, and Scalable Computation of Aggregate Statistics (2017, background): “That said, there have been great advances in practical general-purpose MPC protocols of late [12,13,23,45,46,73,89,91,93,98].”
A New Approach to Practical Active-Secure Two-Party Computation (2012, methodology): “…the practical overhead of secure computation, and several works have perfected and implemented protocols based on Yao gar-bled circuits [MNPS04,BDNP08,LPS08,KS08,PSSW09,HKS + 10,MK10,LP11,SS11, A brief comparison of the time needed for oblivious AES evaluation for the best known…”
Implementing AES via an Actively/Covertly Secure Dishonest-Majority MPC Protocol (2012, background): “In the last few years a number of MPC “systems” have appeared [4, 7–9, 12, 15, 22], as well as experimental research results [13, 16, 21, 25, 26].”
Differentially private data aggregation with optimal utility (2014, methodology): “We could in principle adopt other kinds of SMPC protocols that do not require this assumption [11,31,42], but they are currently less eﬃcient.”
Making argument systems for outsourced computation practical (sometimes) (2012, methodology): “Moreover, while general-purpose compilers for such protocols exist [13, 44, 51], they are not yet practical, despite the striking improvements reported in [44].”
L1 - An Intermediate Language for Mixed-Protocol Secure Computation (2011, background): “Later others followed: [33] for [10], [31] for [5], and [4] for [3].”
Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations (2015, unknown): “The baseline method is applicable to the two-party setting, however it can be extended to the case with more parties [4].”
Fast and Secure Three-party Computation: The Garbled Circuit Approach (2015, background): “An implementation exists [BDNP08] for the semi-honest case.”
Machine Learning Classification over Encrypted Data (2015, methodology): “A set of generic secure two- or multi-party computation tools have been developed, such as TASTY [6] and Fairplay [7], [8].”
TinyGarble: Highly Compressed and Scalable Sequential Garbled Circuits (2015, background): “The inputs and outputs of each gate are masked such that the party evaluating the GC cannot gain any information about the inputs or intermediate results that appear during function evaluation.”
SoK: General Purpose Compilers for Secure Multi-Party Computation (2019, methodology): “Fairplay was subsequently extended to allow for true multi-party computation in FairplayMP [14], using a modified version of the BMR protocol [9].”
P4P: Practical Large-Scale Privacy-Preserving Distributed Computation Robust against Malicious Users (2010, background): “Moreover, addition is extremely easy to parallelize so aggregating a large amount of numbers on a cluster is straightforward.”
Satisfiability modulo counting: a new approach for analyzing privacy properties (2014, methodology): “• We apply countersat to the veriﬁcation of a set of programs from the Fairplay secure compiler project [8].”
Secure Multiparty Computation from SGX (2017, methodology): “Fairplay is a system originally developed to support two-party computation [32] and then extended to FairplayMP to support multiparty computation [6]: Fairplay implements a two party computation protocol in the manner suggested by Yao; FairplayMP is based on the Beaver-Micali-Rogaway protocol.”
Succinct Non-Interactive Zero Knowledge for a von Neumann Architecture (2014, background): “Most prior work only supports restricted classes of programs: [66, 64] present a circuit generator based on Fairplay [53, 12], whose SFDL language does not support important primitives and has inefficient support for others; [56] present a circuit generator for programs without data dependencies (po”