Fast Cut-and-Choose-Based Protocols for Malicious and Covert Adversaries

By Yehuda Lindell. 2013

226 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

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

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data (2017, background): “The general approach for constructing two-party computation based on garbled circuits that provides security against malicious adversaries employs the so-called cut-and-choose technique [51,47].”
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].”
I-Pic: A Platform for Privacy-Compliant Image Capture (2016, background): “This requirement could be relaxed if one is willing to run additional checks [24] at some additional computational and runtime overhead.”
A Pragmatic Introduction to Secure Multi-Party Computation (2019, methodology): “An approach proposed by Lindell and Pinkas (2007) (and improved in shelat and Shen (2013)) uses what are called k-probe-resistant matrices.”
XONN: XNOR-based Oblivious Deep Neural Network Inference (2019, background): “One of the main distinctions between X ONN and the state-of-the-art solutions is that X ONN can be automatically adapted to the malicious security using cut-and-choose techniques [29, 35, 37].”
Faster Secure Two-Party Computation in the Single-Execution Setting (2017, methodology): “For example, the schemes of Lindell [24] and Afshar et al.”
TumbleBit: An Untrusted Bitcoin-Compatible Anonymous Payment Hub (2017, methodology): “(We follow the blueprint of Lindell’s recent work [32].”