Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications

While the practicality of secure multi-party computation (MPC) has been extensively analyzed and improved over the past decade, we are hitting the limits of efficiency with the traditional approaches of representing the computed functionalities as generic arithmetic or Boolean circuits. This work fo...

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Autores principales:	Lu Donghang, Yu Albert, Kate Aniket, Maji Hemanta
Formato:	article
Lenguaje:	EN
Publicado:	Sciendo 2022
Materias:	secure multi-party computation polynomial evaluation privacy-preserving decision tree evaluation Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/063828fcc93e4380a595dfcfa822cf12
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id	oai:doaj.org-article:063828fcc93e4380a595dfcfa822cf12
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spelling	oai:doaj.org-article:063828fcc93e4380a595dfcfa822cf122021-12-05T14:11:10ZPolymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications2299-098410.2478/popets-2022-0020https://doaj.org/article/063828fcc93e4380a595dfcfa822cf122022-01-01T00:00:00Zhttps://doi.org/10.2478/popets-2022-0020https://doaj.org/toc/2299-0984While the practicality of secure multi-party computation (MPC) has been extensively analyzed and improved over the past decade, we are hitting the limits of efficiency with the traditional approaches of representing the computed functionalities as generic arithmetic or Boolean circuits. This work follows the design principle of identifying and constructing fast and provably-secure MPC protocols to evaluate useful high-level algebraic abstractions; thus, improving the efficiency of all applications relying on them. We present Polymath, a constant-round secure computation protocol suite for the secure evaluation of (multi-variate) polynomials of scalars and matrices, functionalities essential to numerous data-processing applications. Using precise natural precomputation and high-degree of parallelism prevalent in the modern computing environments, Polymath can make latency of secure polynomial evaluations of scalars and matrices independent of polynomial degree and matrix dimensions.Lu DonghangYu AlbertKate AniketMaji HemantaSciendoarticlesecure multi-party computationpolynomial evaluationprivacy-preserving decision tree evaluationEthicsBJ1-1725Electronic computers. Computer scienceQA75.5-76.95ENProceedings on Privacy Enhancing Technologies, Vol 2022, Iss 1, Pp 396-416 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	secure multi-party computation polynomial evaluation privacy-preserving decision tree evaluation Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95
spellingShingle	secure multi-party computation polynomial evaluation privacy-preserving decision tree evaluation Ethics BJ1-1725 Electronic computers. Computer science QA75.5-76.95 Lu Donghang Yu Albert Kate Aniket Maji Hemanta Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications
description	While the practicality of secure multi-party computation (MPC) has been extensively analyzed and improved over the past decade, we are hitting the limits of efficiency with the traditional approaches of representing the computed functionalities as generic arithmetic or Boolean circuits. This work follows the design principle of identifying and constructing fast and provably-secure MPC protocols to evaluate useful high-level algebraic abstractions; thus, improving the efficiency of all applications relying on them. We present Polymath, a constant-round secure computation protocol suite for the secure evaluation of (multi-variate) polynomials of scalars and matrices, functionalities essential to numerous data-processing applications. Using precise natural precomputation and high-degree of parallelism prevalent in the modern computing environments, Polymath can make latency of secure polynomial evaluations of scalars and matrices independent of polynomial degree and matrix dimensions.
format	article
author	Lu Donghang Yu Albert Kate Aniket Maji Hemanta
author_facet	Lu Donghang Yu Albert Kate Aniket Maji Hemanta
author_sort	Lu Donghang
title	Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications
title_short	Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications
title_full	Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications
title_fullStr	Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications
title_full_unstemmed	Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications
title_sort	polymath: low-latency mpc via secure polynomial evaluations and its applications
publisher	Sciendo
publishDate	2022
url	https://doaj.org/article/063828fcc93e4380a595dfcfa822cf12
work_keys_str_mv	AT ludonghang polymathlowlatencympcviasecurepolynomialevaluationsanditsapplications AT yualbert polymathlowlatencympcviasecurepolynomialevaluationsanditsapplications AT kateaniket polymathlowlatencympcviasecurepolynomialevaluationsanditsapplications AT majihemanta polymathlowlatencympcviasecurepolynomialevaluationsanditsapplications
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Polymath: Low-Latency MPC via Secure Polynomial Evaluations and Its Applications

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