Appointed-weight Privacy-preserving Consensus of Multi-agent Systems

Yang Zhong; Yuezu Lv

doi:10.1109/ICNC52316.2021.9608393

Appointed-weight Privacy-preserving Consensus of Multi-agent Systems

Yang Zhong, Yuezu Lv^*

^*Corresponding author for this work

Southeast University, Nanjing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Though privacy-preserving consensus of multi-agent systems has been extensively studied, one issue remains unsolved. That is, the final consensus value may not be exactly the average of all the agents' initial values. In this paper, we investigate the privacy-preserving consensus problem of multiagent systems, where the final consensus value is the weighted average of all the agents' initial values with predefined weights. A noise generation mechanism is designed, and a novel privacy-preserving consensus algorithm is proposed. Theoretical analysis shows that the proposed algorithm can realize appointed-weight consensus while achieving privacy preserving.

Original language	English
Title of host publication	2021 International Conference on Neuromorphic Computing, ICNC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	297-301
Number of pages	5
ISBN (Electronic)	9781665412872
DOIs	https://doi.org/10.1109/ICNC52316.2021.9608393
Publication status	Published - 2021
Externally published	Yes
Event	2021 International Conference on Neuromorphic Computing, ICNC 2021 - Wuhan, China Duration: 15 Oct 2021 → 17 Oct 2021

Publication series

Name	2021 International Conference on Neuromorphic Computing, ICNC 2021

Conference

Conference	2021 International Conference on Neuromorphic Computing, ICNC 2021
Country/Territory	China
City	Wuhan
Period	15/10/21 → 17/10/21

Keywords

Appointed-weight consensus
privacy preserving
random noise

Access to Document

10.1109/ICNC52316.2021.9608393

Cite this

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title = "Appointed-weight Privacy-preserving Consensus of Multi-agent Systems",

abstract = "Though privacy-preserving consensus of multi-agent systems has been extensively studied, one issue remains unsolved. That is, the final consensus value may not be exactly the average of all the agents' initial values. In this paper, we investigate the privacy-preserving consensus problem of multiagent systems, where the final consensus value is the weighted average of all the agents' initial values with predefined weights. A noise generation mechanism is designed, and a novel privacy-preserving consensus algorithm is proposed. Theoretical analysis shows that the proposed algorithm can realize appointed-weight consensus while achieving privacy preserving.",

keywords = "Appointed-weight consensus, privacy preserving, random noise",

author = "Yang Zhong and Yuezu Lv",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Neuromorphic Computing, ICNC 2021 ; Conference date: 15-10-2021 Through 17-10-2021",

year = "2021",

doi = "10.1109/ICNC52316.2021.9608393",

language = "English",

series = "2021 International Conference on Neuromorphic Computing, ICNC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "297--301",

booktitle = "2021 International Conference on Neuromorphic Computing, ICNC 2021",

address = "United States",

}

Zhong, Y & Lv, Y 2021, Appointed-weight Privacy-preserving Consensus of Multi-agent Systems. in 2021 International Conference on Neuromorphic Computing, ICNC 2021. 2021 International Conference on Neuromorphic Computing, ICNC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 297-301, 2021 International Conference on Neuromorphic Computing, ICNC 2021, Wuhan, China, 15/10/21. https://doi.org/10.1109/ICNC52316.2021.9608393

Appointed-weight Privacy-preserving Consensus of Multi-agent Systems. / Zhong, Yang; Lv, Yuezu.
2021 International Conference on Neuromorphic Computing, ICNC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 297-301 (2021 International Conference on Neuromorphic Computing, ICNC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhong, Yang

AU - Lv, Yuezu

PY - 2021

Y1 - 2021

N2 - Though privacy-preserving consensus of multi-agent systems has been extensively studied, one issue remains unsolved. That is, the final consensus value may not be exactly the average of all the agents' initial values. In this paper, we investigate the privacy-preserving consensus problem of multiagent systems, where the final consensus value is the weighted average of all the agents' initial values with predefined weights. A noise generation mechanism is designed, and a novel privacy-preserving consensus algorithm is proposed. Theoretical analysis shows that the proposed algorithm can realize appointed-weight consensus while achieving privacy preserving.

AB - Though privacy-preserving consensus of multi-agent systems has been extensively studied, one issue remains unsolved. That is, the final consensus value may not be exactly the average of all the agents' initial values. In this paper, we investigate the privacy-preserving consensus problem of multiagent systems, where the final consensus value is the weighted average of all the agents' initial values with predefined weights. A noise generation mechanism is designed, and a novel privacy-preserving consensus algorithm is proposed. Theoretical analysis shows that the proposed algorithm can realize appointed-weight consensus while achieving privacy preserving.

KW - Appointed-weight consensus

KW - privacy preserving

KW - random noise

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Y2 - 15 October 2021 through 17 October 2021

ER -

Appointed-weight Privacy-preserving Consensus of Multi-agent Systems

Abstract

Publication series

Conference

Keywords

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