Verifiable coded computation of multiple functions

We consider the problem of evaluating distinct multivariate polynomials over several massive datasets in a distributed computing system with a single master node and multiple worker nodes. We focus on the general case when each multivariate polynomial is evaluated over its corresponding dataset and...

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Main Authors: Kim, Wilton, Kruglik, Stanislav, Kiah, Han Mao
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/180653
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1806532024-10-21T15:35:05Z Verifiable coded computation of multiple functions Kim, Wilton Kruglik, Stanislav Kiah, Han Mao School of Physical and Mathematical Sciences Computer and Information Science Distributed computing Communication efficiency Verifiability Privacy We consider the problem of evaluating distinct multivariate polynomials over several massive datasets in a distributed computing system with a single master node and multiple worker nodes. We focus on the general case when each multivariate polynomial is evaluated over its corresponding dataset and propose a generalization of the Lagrange Coded Computing framework (Yu et al. 2019) to perform all computations simultaneously while providing robustness against stragglers who do not respond in time, adversarial workers who respond with wrong computation and information-theoretic security of dataset against colluding workers. Our scheme introduces a small computation overhead which results in a reduction in download cost and also offers comparable resistance to stragglers over existing solutions. On top of it, we also propose two verification schemes to detect the presence of adversaries, which leads to incorrect results, without involving additional nodes. Ministry of Education (MOE) National Research Foundation (NRF) Submitted/Accepted version This research is supported by the National Research Foundation, Singapore under its Strategic Capability Research Centres Funding Initiative, Singapore Ministry of Education Academic Research Fund Tier 2 Grants MOE2019-T2-2-083, MOE-T2EP20121-0007 and Academic Research Fund Tier 1 Grant RG19/23. 2024-10-17T00:59:01Z 2024-10-17T00:59:01Z 2024 Journal Article Kim, W., Kruglik, S. & Kiah, H. M. (2024). Verifiable coded computation of multiple functions. IEEE Transactions On Information Forensics and Security, 19, 8009-8022. https://dx.doi.org/10.1109/TIFS.2024.3450288 1556-6013 https://hdl.handle.net/10356/180653 10.1109/TIFS.2024.3450288 19 8009 8022 en MOE2019-T2-2-083 MOE-T2EP20121-0007 RG19/23 IEEE Transactions on Information Forensics and Security © 2024 IEEE. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1109/TIFS.2024.3450288. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Distributed computing
Communication efficiency
Verifiability
Privacy
spellingShingle Computer and Information Science
Distributed computing
Communication efficiency
Verifiability
Privacy
Kim, Wilton
Kruglik, Stanislav
Kiah, Han Mao
Verifiable coded computation of multiple functions
description We consider the problem of evaluating distinct multivariate polynomials over several massive datasets in a distributed computing system with a single master node and multiple worker nodes. We focus on the general case when each multivariate polynomial is evaluated over its corresponding dataset and propose a generalization of the Lagrange Coded Computing framework (Yu et al. 2019) to perform all computations simultaneously while providing robustness against stragglers who do not respond in time, adversarial workers who respond with wrong computation and information-theoretic security of dataset against colluding workers. Our scheme introduces a small computation overhead which results in a reduction in download cost and also offers comparable resistance to stragglers over existing solutions. On top of it, we also propose two verification schemes to detect the presence of adversaries, which leads to incorrect results, without involving additional nodes.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Kim, Wilton
Kruglik, Stanislav
Kiah, Han Mao
format Article
author Kim, Wilton
Kruglik, Stanislav
Kiah, Han Mao
author_sort Kim, Wilton
title Verifiable coded computation of multiple functions
title_short Verifiable coded computation of multiple functions
title_full Verifiable coded computation of multiple functions
title_fullStr Verifiable coded computation of multiple functions
title_full_unstemmed Verifiable coded computation of multiple functions
title_sort verifiable coded computation of multiple functions
publishDate 2024
url https://hdl.handle.net/10356/180653
_version_ 1814777782346973184