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Privacy Preserving Average Consensus

Mo, Yilin and Murray, Richard M. (2017) Privacy Preserving Average Consensus. IEEE Transactions on Automatic Control, 62 (2). pp. 753-765. ISSN 0018-9286.

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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 an agent to the other agents. In this paper, we propose a privacy preserving average consensus algorithm to guarantee the privacy of the initial state and asymptotic consensus on 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 the covariance matrix of the maximum likelihood estimate on the initial state. Moreover, we prove that our proposed algorithm is optimal in the sense that it does not disclose any information more than necessary to achieve the average consensus. A numerical example is provided to illustrate the effectiveness of the proposed design.

Item Type:Article
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Murray, Richard M.0000-0002-5785-7481
Additional Information:© 2016 IEEE. Manuscript received June 16, 2015; revised January 25, 2016 and January 27, 2016; accepted April 25, 2016. Date of publication May 5, 2016; date of current version January 26, 2017. This work is supported in part by IBM and UTC.
Funding AgencyGrant Number
Subject Keywords:Estimation, multi-agent systems, networked control systems, privacy
Issue or Number:2
Record Number:CaltechAUTHORS:20170202-080959480
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Official Citation:Y. Mo and R. M. Murray, "Privacy Preserving Average Consensus," in IEEE Transactions on Automatic Control, vol. 62, no. 2, pp. 753-765, Feb. 2017. doi: 10.1109/TAC.2016.2564339 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:73977
Deposited By: Tony Diaz
Deposited On:02 Feb 2017 17:40
Last Modified:03 Oct 2019 16:33

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