Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86339
Title: Privacy preserving average consensus
Authors: Mo, Yilin
Murray, Richard M.
Keywords: Vectors
Privacy
Issue Date: 2014
Source: Mo, Y., & Murray, R. M. (2014). Privacy preserving average consensus. 53rd IEEE Conference on Decision and Control.
Abstract: 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.
URI: https://hdl.handle.net/10356/86339
http://hdl.handle.net/10220/44020
DOI: 10.1109/CDC.2014.7039717
Rights: © 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].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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