High-Throughput Secure Three-Party Computation for Malicious Adversaries and an Honest Majority

By Jun Furukawa, Yehuda Lindell, Ariel Nof, Or Weinstein. 2017

190 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems, Privacy & Security

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “as kicked off in the 1980’s by Yao [422]. Thanks to both theoretical and engineering breakthroughs, the ﬁeld has moved from being of a purely theoretical interest to a deployed technology in industry [71, 70, 257, 29, 169, 209, 210]. It is important to remark that MPC deﬁnes a set of technologies, a”
Secure Evaluation of Quantized Neural Networks (2019, methodology): “Prime order field Due to capabilities of MP-SPDZ, we could also run our benchmarks in replicated secret sharing modulo a 128-bit prime, both with semi-honest and malicious security [20].”
Federated Machine Learning (2019, background): “Ref [44] proposed a 3PC model [5, 21, 45] with an honest majority and consider security in both semi-honest and malicious assumptions.”
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications (2018, neutral): “Recent work of [3, 4, 43] introduces an efficient protocol for three-party secure computation.”
Generalizing the SPDZ Compiler For Other Protocols (2018, methodology): “The protocol of [4] for the Boolean case was extended to deal with malicious adversaries in [3, 16].”
Global-Scale Secure Multiparty Computation (2017, background): “[21] also presented a malicious 3PC protocol with honest majority.”
An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies (2020, background): “Other than two-party computation, three-party communication have been proposed as-well [60]–[63].”
Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning (2020, methodology): “F Mult , F Trunc , F Reconst are identical to prior works [8, 13].”
Optimized Honest-Majority MPC for Malicious Adversaries — Breaking the 1 Billion-Gate Per Second Barrier (2017, methodology): “Security in the presence of malicious adversaries is achieved in [11] by using the cut-and-choose technique in order to generate many valid multiplication triples (shares of secret bits (a, b, c) where a, b are random and c = ab).”
Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning (2019, background): “Similar setup for the three-party case can be found in [4], [5], [11], [12], [25].”
ABY3: A Mixed Protocol Framework for Machine Learning (2018, methodology): “Hence, we follow the same security definitions and refer to [9] and [28] for a formal description of these adversarial settings.”
SecureNN: 3-Party Secure Computation for Neural Network Training (2019, background): “Secure multi-party computation (MPC) [8, 17, 25, 40] and specifically 3-party computation [6, 9, 10, 14, 18, 23, 28, 32] provide a generic solution for the above problem.”
A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority (2017, methodology): “In the full version of this paper, we describe a protocol for generating multiplication triples in small elds, that combines the veri cation method of Section 4 together with the “cut-andchoose” methodology designed for Boolean circuits in [19].”
A Pragmatic Introduction to Secure Multi-Party Computation (2019, methodology): “The GMW protocol was introduced by Goldreich et al. (1987), but Goldreich (2004) provides a cleaner and more detailed presentation.”
On Deploying Secure Computing Commercially: Private Intersection-Sum Protocols and their Business Applications (2019, background): “[3, 26] deploy secure computation for cryptographic operations running between machines owned by a single organization.”