Share Conversion, Pseudorandom Secret-Sharing and Applications to Secure Computation

By Ronald Cramer, Ivan Bjerre Damgård, Yuval Ishai. 2005

312 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability

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Citations in the corpus

Secure Evaluation of Quantized Neural Networks (2019, background): “Moreover, the sharing of zero above can be preprocessed in a very efficient manner by an initial set-up phase in which each Pi sends a key to Pi+1 for a pseudorandom function [15, 20].”
FairplayMP: a system for secure multi-party computation (2008, methodology): “In Step 1 each computation player receives shares of a (bn/2c + 1)-out-of-n secret sharing scheme, and engages in the bit-flipping protocol of [7].”
Provably Secure Higher-Order Masking of AES (2010, background): “Actually, this approach is very close to the problem of defining Multi Party Communication (MPC) schemes (see for instance [9, 12]) but the resources and constraints differ in the two contexts (e.”
A Framework for Constructing Fast MPC over Arithmetic Circuits with Malicious Adversaries and an Honest-Majority (2017, methodology): “The rst method of generating random shares is the PRSS method of [13], which enables the parties to generate a sharing of a pseudorandom element without any interaction.”
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].”
New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning (2019, background): “On the theoretical side, share conversion between different secret-sharing schemes was ﬁrst studied by Cramer, Damg ˚ ard and Ishai [27].”
Efficient, Robust and Constant-Round Distributed RSA Key Generation (2010, background): “pseudo random secret sharing (PRSS) [9] in a novel way, this can be implemented as a noninteractive protocol.”
Zero-Knowledge Proofs on Secret-Shared Data via Fully Linear PCPs (2019, methodology): “Moreover, such shares can then be locally converted into Shamir shares without any interaction [49, 68].”
Differentially private data aggregation with optimal utility (2014, methodology): “The following SMPC protocols [8, 13, 17, 25] are used for our DDP mechanisms.”
Secure Multiparty Sorting Protocols with Covert Privacy (2016, background): “It introduces the replicated secret sharing [11] JJyKK of a value y ∈ ZN .”
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning (2020, background): “(3)The random parameters can be generated by a crypto-service provider in an offline manner, or by using pseudo-random secret sharing [9].”
Improved Primitives for Secure Multiparty Integer Computation (2010, methodology): “A scalable solution is to run the protocols on a small number of semi-trusted servers [7,12].”