Fairplay - Secure Two-Party Computation System

By Dahlia Malkhi, Noam Nisan, Benny Pinkas, Yaron Sella. 2004

865 citations recorded by the source corpus.

How researchers see this paper

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

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].”
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records (2013, background): “[15] for an efficient implementation.”
Pinocchio: Nearly Practical Verifiable Computation (2013, background): “Secure multiparty computation, for example, has seen tremendous progress [57–59].”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, methodology): “The first implementation of the GC protocol is Fairplay [69] that allows users to write the program in a high-level language called Secure Function Definition Language (SFDL) which is translated into a Boolean circuit.”
Secure Multiparty Computation for Privacy-Preserving Data Mining (2009, methodology): “We note that an implementation of Yao’s protocol exists (the Fairplay project (34)).”
Verifying computations with state (2013, background): “3), into a list of assignment statements and then outputs a constraint or pseudoconstraint for each statement (the compiler is derived from Fairplay’s [35] and is described elsewhere [9, 44]).”
Rmind: A Tool for Cryptographically Secure Statistical Analysis (2016, background): “Some provide secure integer arithmetic [20], [21], [22], [23], [24], [25], [26], floating point arithmetic [27], [28], shuffling and sorting [29] and linking [4].”
Secure Multiparty Computation (2011, unknown): “Furthermore, Yao’s scheme has been implemented in the FAIRPLAY system [22], and it was shown that this scheme is practical for some problems.”
Faster Secure Two-Party Computation Using Garbled Circuits (2011, methodology): “Using standard techniques [22], the construction can be modiﬁed so a single decryption sufﬁces.”
CrypTFlow: Secure TensorFlow Inference (2019, background): “include Fairplay [56], Wysteria [70], ObliVM [54], CBMC-”
L1 - An Intermediate Language for Mixed-Protocol Secure Computation (2011, methodology): “FairPlay [26] was the first compiler implementing SC.”
Security Against Covert Adversaries: Efficient Protocols for Realistic Adversaries (2007, methodology): “We remark that the idea of allowing an adversary to cheat with non-negligible probability as long as it will be caught with good probability has been mentioned many times in the literature; see [15,20] for just two examples.”
Compacting privacy-preserving k-nearest neighbor search using logic synthesis (2015, methodology): “In the area of mapping and optimizing functions into Boolean logic for GC, the related literature has suggested developing custom compilers [9,11,19,23], custom libraries for conventional high-level compilers [10, 14, 22], hardware accelerators[2, 16,27], and mobile device implementation [24].”
Privacy-Preserving Backpropagation Neural Network Learning (2009, methodology): “(We utilize the Fairplay secure function evaluation system [37]”
Quid-Pro-Quo-tocols: Strengthening Semi-honest Protocols with Dual Execution (2012, methodology): “…including the point-and-permute technique that allows the circuit evaluator to decrypt only a single entry (rather than all four) in a garbled gate [29]; the free-XOR technique [23] that allows XOR gates to be executed without any encryption operations; Garbled Row Reduction (GRR) [34] that…”
Practical verified computation with streaming interactive proofs (2011, methodology): “The circuit checking approach can be combined with existing compilers, such as that in the Fairplay system [26], that take as input a program in a high-level programming language and output a corresponding boolean circuit.”