Non-interactive multiparty computation without correlated randomness

Shai Halevi, Yuval Ishai, Abhishek Jain, Ilan Komargodski, Amit Sahai, Eylon Yogev

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

25 Scopus citations

Abstract

We study the problem of non-interactive multiparty computation (NI-MPC) where a group of completely asynchronous parties can evaluate a function over their joint inputs by sending a single message to an evaluator who computes the output. Previously, the only general solutions to this problem that resisted collusions between the evaluator and a set of parties were based on multi-input functional encryption and required the use of complex correlated randomness setup. In this work, we present a new solution for NI-MPC against arbitrary collusions using a public-key infrastructure (PKI) setup supplemented with a common random string. A PKI is, in fact, the minimal setup that one can hope for in this model in order to achieve a meaningful “best possible” notion of security, namely, that an adversary that corrupts the evaluator and an arbitrary set of parties only learns the residual function obtained by restricting the function to the inputs of the uncorrupted parties. Our solution is based on indistinguishability obfuscation and DDH both with sub-exponential security. We extend this main result to the case of general interaction patterns, providing the above best possible security that is achievable for the given interaction. Our main result gives rise to a novel notion of (public-key) multiparty obfuscation, where n parties can independently obfuscate program modules Mi such that the obfuscated modules, when put together, exhibit the functionality of the program obtained by “combining” the underlying modules Mi. This notion may be of independent interest.

Original languageEnglish
Title of host publicationAdvances in Cryptology – ASIACRYPT 2017 - 23rd International Conference on the Theory and Applications of Cryptology and Information Security, Proceedings
EditorsTsuyoshi Takagi, Thomas Peyrin
PublisherSpringer Verlag
Pages181-211
Number of pages31
ISBN (Print)9783319706993
DOIs
StatePublished - 2017
Externally publishedYes
Event23rd Annual International Conference on Theory and Application of Cryptology and Information Security, ASIACRYPT 2017 - Hong Kong, Hong Kong
Duration: 3 Dec 20177 Dec 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10626 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd Annual International Conference on Theory and Application of Cryptology and Information Security, ASIACRYPT 2017
Country/TerritoryHong Kong
CityHong Kong
Period3/12/177/12/17

Bibliographical note

Publisher Copyright:
© International Association for Cryptologic Research 2017.

Funding

Acknowledgments. Shai Halevi was supported by the Defense Advanced Research Projects Agency (DARPA) and Army Research Office (ARO) under Contract No. W911NF-15-C-0236. Yuval Ishai was supported in part by NSF-BSF grant 2015782, BSF grant 2012366, ISF grant 1709/14, ERC grant 742754, DARPA/ARL SAFEWARE award, NSF Frontier Award 1413955, NSF grants 1619348, 1228984, 1136174, and 1065276, a Xerox Faculty Research Award, a Google Faculty Research Award, an equipment grant from Intel, and an Okawa Foundation Research Grant. This material is based upon work supported by the DARPA through the ARL under Contract W911NF-15-C-0205. Abhishek Jain was supported in part by a DARPA/ARL Safeware Grant W911NF-15-C-0213 and a sub-award from NSF CNS-1414023. Ilan Komargodski is supported in part by Elaine Shi’s Packard Foundation Fellowship. Most of this work done while he was a Ph.D student at the Weizmann Institute of Science, supported in part by grants from the Israel Science Foundation and by a Levzion Fellowship. Amit Sahai was supported in part from a DARPA/ARL SAFEWARE award, NSF Frontier Award 1413955, NSF grants 1619348, 1228984, 1136174, and 1065276, BSF grant 2012378, a Xerox Faculty Research Award, a Google Faculty Research Award, an equipment grant from Intel, and an Okawa Foundation Research Grant. This material is based upon work supported by the Defense Advanced Research Projects Agency through the ARL under Contract W911NF-15-C-0205. Eylon Yogev is supported in part by a grant from the Israel Science Foundation. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense, the National Science Foundation, or the U.S. Government.

FundersFunder number
Elaine Shi’s Packard Foundation
NSF-BSF
Okawa FoundationW911NF-15-C-0205, CNS-1414023
National Science Foundation1228984, 1413955, 2015782, 1065276, 1136174, 1619348
Army Research OfficeW911NF-15-C-0236
Defense Advanced Research Projects AgencyW911NF-15-C-0213
Bonfils-Stanton Foundation2012366
Intel Corporation
Army Research Laboratory
Google
Engineering Research Centers742754
Weizmann Institute of Science
Israel Science Foundation1709/14, 2012378

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