Consensus with preserved privacy against neighbor collusion

This paper proposes a privacy-preserving algorithm to solve the average-consensus problem based on Shamir’s secret sharing scheme, in which a network of agents reach an agreement on their states without exposing their individual states until an agreement is reached. Unlike other methods, the propose...

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Main Authors: Zhang, Silun, Ohlson Timoudas, Thomas, Dahleh, Munther A
Other Authors: Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Format: Article
Language:English
Published: Springer Science and Business Media LLC 2021
Online Access:https://hdl.handle.net/1721.1/128922
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author Zhang, Silun
Ohlson Timoudas, Thomas
Dahleh, Munther A
author2 Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
author_facet Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Zhang, Silun
Ohlson Timoudas, Thomas
Dahleh, Munther A
author_sort Zhang, Silun
collection MIT
description This paper proposes a privacy-preserving algorithm to solve the average-consensus problem based on Shamir’s secret sharing scheme, in which a network of agents reach an agreement on their states without exposing their individual states until an agreement is reached. Unlike other methods, the proposed algorithm renders the network resistant to the collusion of any given number of neighbors (even with all neighbors’ colluding). Another virtue of this work is that such a method can protect the network consensus procedure from eavesdropping.
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spelling mit-1721.1/1289222022-09-29T19:13:22Z Consensus with preserved privacy against neighbor collusion Zhang, Silun Ohlson Timoudas, Thomas Dahleh, Munther A Massachusetts Institute of Technology. Laboratory for Information and Decision Systems This paper proposes a privacy-preserving algorithm to solve the average-consensus problem based on Shamir’s secret sharing scheme, in which a network of agents reach an agreement on their states without exposing their individual states until an agreement is reached. Unlike other methods, the proposed algorithm renders the network resistant to the collusion of any given number of neighbors (even with all neighbors’ colluding). Another virtue of this work is that such a method can protect the network consensus procedure from eavesdropping. 2021-01-04T14:49:41Z 2021-01-04T14:49:41Z 2020-12 2020-09 2020-12-16T04:29:51Z Article http://purl.org/eprint/type/JournalArticle 2095-6983 2198-0942 https://hdl.handle.net/1721.1/128922 Zhang, Silun et al. "Consensus with preserved privacy against neighbor collusion." Control Theory and Technology 18, 4 (December 2020): 409–418 © 2020, South China University of Technology, Academy of Mathematics and Systems Science, CAS and Springer-Verlag GmbH Germany, part of Springer Nature en http://dx.doi.org/10.1007/s11768-020-00023-x Control Theory and Technology Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ South China University of Technology, Academy of Mathematics and Systems Science, CAS and Springer-Verlag GmbH Germany, part of Springer Nature application/pdf Springer Science and Business Media LLC South China University of Technology and Academy of Mathematics and Systems Science, CAS
spellingShingle Zhang, Silun
Ohlson Timoudas, Thomas
Dahleh, Munther A
Consensus with preserved privacy against neighbor collusion
title Consensus with preserved privacy against neighbor collusion
title_full Consensus with preserved privacy against neighbor collusion
title_fullStr Consensus with preserved privacy against neighbor collusion
title_full_unstemmed Consensus with preserved privacy against neighbor collusion
title_short Consensus with preserved privacy against neighbor collusion
title_sort consensus with preserved privacy against neighbor collusion
url https://hdl.handle.net/1721.1/128922
work_keys_str_mv AT zhangsilun consensuswithpreservedprivacyagainstneighborcollusion
AT ohlsontimoudasthomas consensuswithpreservedprivacyagainstneighborcollusion
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