High-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority

By Toshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof, Kazuma Ohara. 2016

470 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Privacy & Security

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Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “There is a rich literature exploring secure aggregation in both the single-server setting (via pairwise additive masking [12, 188, 73], via threshold homomorphic encryption [356, 193, 92], and via generic secure multi-party computation [86]) as well as in the multiple non-colluding servers setting [”
Federated Machine Learning (2019, background): “The authors of [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, background): “Recent work of [3, 4, 40] introduces an efficient protocol for three-party secure computation.”
Global-Scale Secure Multiparty Computation (2017, unknown): “[2]; these each tolerate only semi-honest behavior.”
CrypTFlow: Secure TensorFlow Inference (2019, background): “[8]) informally guarantees that privacy of inputs hold even against malicious party as long as none of the parties participating in the protocol learn the output of the computation (this is relevant for example, when computation is ofﬂoaded to servers).”
Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning (2020, methodology): “This is a common adversarial setting considered in previous secure multi-party computation approaches [4, 11, 21, 30].”
Private Collaborative Neural Network Learning (2017, methodology): “For example, the size of the circuit can go down to 930 bytes when using the protocol of Araki et al. [2016].”
A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority (2017, methodology): “Our replicated secret-sharing is a simpli ed version of that presented in [3], and the multiplication protocol has the same complexity as theirs.”
High-Throughput Secure Three-Party Computation for Malicious Adversaries and an Honest Majority (2017, methodology): “In fact, the secret-sharing method used in [1] is such that it is possible to generate shares of random values without any interaction (using correlated randomness which is generated by the parties at almost no cost).”
MP-SPDZ: A Versatile Framework for Multi-Party Computation (2020, background): “[5] have observed that resharing can be done by every party sending exactly one element of the computation domain by using pseudo-random zero sharing [24].”
Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning (2019, background): “While MPC has been shown to be practical [7]– [9], MPC for a small number of parties in the honest majority setting [10]–[15], [15]–[19] has become popular over the last few years due to applications such as financial data analysis [20], email spam filtering [21], distributed credential encryption [”
Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017, background): “recently presented a generic three-party computation protocol that achieves very high throughput [5].”
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.”
Generalizing the SPDZ Compiler For Other Protocols (2018, methodology): “Furthermore, the protocol of [3] achieves security in the presence of malicious adversaries in this setting at the cost of just 7 times that of [4] (i.”