Open Privacy‐Preserving Consensus via State Decomposition

ABSTRACT Generally, the majority of the achievements on privacy preservation of consensus always anchor that the number of interacting agents is permanent. Nevertheless, this is overly restrictive in many multi‐agent systems (MAS) applications where agent removal or joining may arise at any operational instant. In light of this, the current article studies the privacy‐preserving problem of open MAS. To achieve this, we propose an open state‐decomposition‐based algorithm that not only assures the consensus but also achieves the privacy preservation of the initial states, despite the agent removal or joining. It is shown that the proposed algorithm can mask the true values in an open communication environment, thus preventing honest‐but‐curious agents and external eavesdroppers from accessing sensitive information. Finally, the effectiveness of the derived results, as well as the proposed algorithm, are illustrated via numerical simulations.

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Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2025-03-26
DOI
https://doi.org/10.1002/rnc.7929
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.00

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article

Open Privacy‐Preserving Consensus via State Decomposition

Xiaoyu Wu, Dandan Li, Zhicheng Zhang, Zhenqian Wang et al.
International Journal of Robust and Nonlinear Control
Privacy-Preserving Technologies in Data
article

Open Privacy‐Preserving Consensus via State Decomposition

Xiaoyu Wu, Dandan Li, Zhicheng Zhang, Zhenqian Wang, Yan Zhang
article en

Abstract

ABSTRACT Generally, the majority of the achievements on privacy preservation of consensus always anchor that the number of interacting agents is permanent. Nevertheless, this is overly restrictive in many multi‐agent systems (MAS) applications where agent removal or joining may arise at any operational instant. In light of this, the current article studies the privacy‐preserving problem of open MAS. To achieve this, we propose an open state‐decomposition‐based algorithm that not only assures the consensus but also achieves the privacy preservation of the initial states, despite the agent removal or joining. It is shown that the proposed algorithm can mask the true values in an open communication environment, thus preventing honest‐but‐curious agents and external eavesdroppers from accessing sensitive information. Finally, the effectiveness of the derived results, as well as the proposed algorithm, are illustrated via numerical simulations.

International Journal of Robust and Nonlinear Control
Zhejiang Normal University (CN), Tianjin University (CN), Huzhou Normal University (CN), Beihang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 97%
Privacy-Preserving Technologies in Data
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Open Privacy‐Preserving Consensus via State Decomposition — Xiaoyu Wu, Dandan Li, et al. · International Journal of Robust and Nonlinear Control (2025) | TGRS Research Map | TGRS