Efficient protocols for oblivious linear function evaluation from ring-lwe

Carsten Baum, Daniel Escudero, Alberto Pedrouzo-Ulloa, Peter Scholl, Juan Ramón Troncoso-Pastoriza

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

13 Scopus citations

Abstract

An oblivious linear function evaluation protocol, or OLE, is a two-party protocol for the function $$f(x) = ax + b$$, where a sender inputs the field elements a, b, and a receiver inputs x and learns f(x). OLE can be used to build secret-shared multiplication, and is an essential component of many secure computation applications including general-purpose multi-party computation, private set intersection and more. In this work, we present several efficient OLE protocols from the ring learning with errors (RLWE) assumption. Technically, we build two new passively secure protocols, which build upon recent advances in homomorphic secret sharing from (R)LWE (Boyle et al., Eurocrypt 2019), with optimizations tailored to the setting of OLE. We upgrade these to active security using efficient amortized zero-knowledge techniques for lattice relations (Baum et al., Crypto 2018), and design new variants of zero-knowledge arguments that are necessary for some of our constructions. Our protocols offer several advantages over existing constructions. Firstly, they have the lowest communication complexity amongst previous, practical protocols from RLWE and other assumptions; secondly, they are conceptually very simple, and have just one round of interaction for the case of OLE where b is randomly chosen. We demonstrate this with an implementation of one of our passively secure protocols, which can perform more than 1 million OLEs per second over the ring $$\mathbb {Z}_m$$, for a 120-bit modulus m, on standard hardware.

Original languageEnglish
Title of host publicationSecurity and Cryptography for Networks - 12th International Conference, SCN 2020, Proceedings
EditorsClemente Galdi, Vladimir Kolesnikov
PublisherSpringer Science and Business Media Deutschland GmbH
Pages130-149
Number of pages20
ISBN (Print)9783030579890
DOIs
StatePublished - 2020
Externally publishedYes
Event12th International Conference on Security and Cryptography for Networks, SCN 2020 - Amalfi, Italy
Duration: 14 Sep 202016 Sep 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12238 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Security and Cryptography for Networks, SCN 2020
Country/TerritoryItaly
CityAmalfi
Period14/09/2016/09/20

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Funding

Acknowledgements. We thank the anonymous reviewers for comments which helped to improve the paper. This work has been supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 669255 (MPCPRO), the Danish Independent Research Council under Grant-ID DFF–6108-00169 (FoCC), an Aarhus University Research Foundation starting grant, the Xunta de Galicia & ERDF under projects ED431G2019/08 and Grupo de Referencia ED431C2017/53, and by the grant #2017-201 (DPPH) of the Strategic Focal Area “Personalized Health and Related Technologies (PHRT)” of the ETH Domain. We thank the anonymous reviewers for comments which helped to improve the paper. This work has been supported by the European Research Council (ERC) under the European Union?s Horizon 2020 research and innovation programme under grant agreement No 669255 (MPCPRO), the Danish Independent Research Council under Grant-ID DFF?6108-00169 (FoCC), an Aarhus University Research Foundation starting grant, the Xunta de Galicia & ERDF under projects ED431G2019/08 and Grupo de Referencia ED431C2017/53, and by the grant #2017-201 (DPPH) of the Strategic Focal Area ?Personalized Health and Related Technologies (PHRT)? of the ETH Domain.

FundersFunder number
Danish Independent Research Council6108-00169
FoCC
PHRT
Related Technologies
Xunta de Galicia & ERDF
Natur og Univers, Det Frie ForskningsrådDFF–6108-00169
Horizon 2020 Framework Programme669255
European Commission
Aarhus Universitets Forskningsfond
Eidgenössische Technische Hochschule Zürich
Xunta de GaliciaED431C2017/53, 2017-201, ED431G2019/08

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