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Consensus of Linear Multivariable Discrete-Time Multiagent Systems: Differential Privacy Perspective.

Yamin Wang, James Lam, Hong Lin

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    |January 11, 2022
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    Summary
    This summary is machine-generated.

    This study introduces a new method to protect individual privacy in multiagent systems using differential privacy. It ensures agents maintain their privacy while achieving consensus, even with added noise.

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    Area of Science:

    • Control Systems Engineering
    • Information Security
    • Computer Science

    Background:

    • Differential privacy is a robust privacy mechanism widely used in industries to protect individual data.
    • Multiagent systems require secure communication and consensus protocols, which can be vulnerable to privacy breaches.
    • Existing methods may not adequately preserve differential privacy in dynamic multiagent systems.

    Purpose of the Study:

    • To investigate multivariable discrete-time multiagent systems while preserving epsilon-differential privacy.
    • To develop a novel information-masking mechanism for secure state transmission between agents.
    • To establish conditions for mean-square consensus and analyze convergence rates under privacy constraints.

    Main Methods:

    • Proposed a novel information-masking mechanism obscuring state information with independent random noises.
    • Analyzed the mean-square consensus conditions for the multiagent system.
    • Derived upper and lower bounds for the convergence rate and conditions for achieving the upper bound.

    Main Results:

    • Established mean-square consensus conditions for multiagent systems with epsilon-differential privacy.
    • Obtained precise upper and lower bounds for the convergence rate.
    • Identified conditions under which agents maintain epsilon-differential privacy at all times.

    Conclusions:

    • The proposed information-masking mechanism effectively preserves epsilon-differential privacy in multiagent systems.
    • The derived consensus conditions and convergence rate bounds are applicable to average mean-square consensus problems.
    • A necessary and sufficient condition ensures agents' dynamics remain differentially private throughout the system's operation.