Secure massively parallel computation for dishonest majority

Rex Fernando*, Ilan Komargodski, Yanyi Liu, Elaine Shi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This work concerns secure protocols in the massively parallel computation (MPC) model, which is one of the most widely-accepted models for capturing the challenges of writing protocols for the types of parallel computing clusters which have become commonplace today (MapReduce, Hadoop, Spark, etc.). Recently, the work of Chan et al. (ITCS ’20) initiated this study, giving a way to compile any MPC protocol into a secure one in the common random string model, achieving the standard secure multi-party computation definition of security with up to 1/3 of the parties being corrupt. We are interested in achieving security for much more than 1/3 corruptions. To that end, we give two compilers for MPC protocols, which assume a simple public-key infrastructure, and achieve semi-honest security for all-but-one corruptions. Our first compiler assumes hardness of the learning-with-errors (LWE) problem, and works for any MPC protocol with “short” output—that is, where the output of the protocol can fit into the storage space of one machine, for instance protocols that output a trained machine learning model. Our second compiler works for any MPC protocol (even ones with a long output, such as sorting) but assumes, in addition to LWE, indistinguishability obfuscation and a circular secure variant of threshold FHE. Both protocols allow the attacker to choose corrupted parties based on the trusted setup, an improvement over Chan et al., whose protocol requires that the CRS is chosen independently of the attacker’s choices.

Original languageAmerican English
Title of host publicationTheory of Cryptography - 18th International Conference, TCC 2020, Proceedings
EditorsRafael Pass, Krzysztof Pietrzak
PublisherSpringer Science and Business Media Deutschland GmbH
Pages379-409
Number of pages31
ISBN (Print)9783030643775
DOIs
StatePublished - 2020
Event18th International Conference on Theory of Cryptography, TCCC 2020 - Durham, United States
Duration: 16 Nov 202019 Nov 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12551 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Theory of Cryptography, TCCC 2020
Country/TerritoryUnited States
CityDurham
Period16/11/2019/11/20

Bibliographical note

Publisher Copyright:
© International Association for Cryptologic Research 2020.

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