Privacy preserving average consensus

Average consensus is a widely used algorithm for distributed computing and control, where all the agents in the network constantly communicate and update their states in order to achieve an agreement. This approach could result in an undesirable disclosure of information on the initial state of agen...

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Main Authors: Mo, Yilin, Murray, Richard M.
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2017
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Online Access:https://hdl.handle.net/10356/86339
http://hdl.handle.net/10220/44020
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-863392020-03-07T13:24:45Z Privacy preserving average consensus Mo, Yilin Murray, Richard M. School of Electrical and Electronic Engineering 2014 IEEE 53rd Annual Conference on Decision and Control (CDC) Vectors Privacy Average consensus is a widely used algorithm for distributed computing and control, where all the agents in the network constantly communicate and update their states in order to achieve an agreement. This approach could result in an undesirable disclosure of information on the initial state of agent i to the other agents. In this paper, we propose a privacy preserving average consensus algorithm to guarantee the privacy of the initial state and the convergence of the algorithm to the exact average of the initial values, by adding and subtracting random noises to the consensus process. We characterize the mean square convergence rate of our consensus algorithm and derive upper and lower bounds for the covariance matrix of the maximum likelihood estimate on the initial state. A numerical example is provided to illustrate the effectiveness of the proposed design. Accepted version 2017-11-09T06:21:40Z 2019-12-06T16:20:39Z 2017-11-09T06:21:40Z 2019-12-06T16:20:39Z 2014 Conference Paper Mo, Y., & Murray, R. M. (2014). Privacy preserving average consensus. 53rd IEEE Conference on Decision and Control. https://hdl.handle.net/10356/86339 http://hdl.handle.net/10220/44020 10.1109/CDC.2014.7039717 en © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/CDC.2014.7039717]. 13 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Vectors
Privacy
spellingShingle Vectors
Privacy
Mo, Yilin
Murray, Richard M.
Privacy preserving average consensus
description Average consensus is a widely used algorithm for distributed computing and control, where all the agents in the network constantly communicate and update their states in order to achieve an agreement. This approach could result in an undesirable disclosure of information on the initial state of agent i to the other agents. In this paper, we propose a privacy preserving average consensus algorithm to guarantee the privacy of the initial state and the convergence of the algorithm to the exact average of the initial values, by adding and subtracting random noises to the consensus process. We characterize the mean square convergence rate of our consensus algorithm and derive upper and lower bounds for the covariance matrix of the maximum likelihood estimate on the initial state. A numerical example is provided to illustrate the effectiveness of the proposed design.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Mo, Yilin
Murray, Richard M.
format Conference or Workshop Item
author Mo, Yilin
Murray, Richard M.
author_sort Mo, Yilin
title Privacy preserving average consensus
title_short Privacy preserving average consensus
title_full Privacy preserving average consensus
title_fullStr Privacy preserving average consensus
title_full_unstemmed Privacy preserving average consensus
title_sort privacy preserving average consensus
publishDate 2017
url https://hdl.handle.net/10356/86339
http://hdl.handle.net/10220/44020
_version_ 1681039902986207232