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    This study introduces a new method for private average consensus in multiagent systems (MASs). It ensures data privacy using randomized noise while maintaining agent positivity and achieving accurate consensus.

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    Area of Science:

    • Control Systems Engineering
    • Information Security
    • Distributed Computing

    Background:

    • Multiagent systems (MASs) require secure communication for distributed decision-making.
    • Achieving average consensus in MASs while preserving data privacy is a significant challenge.
    • Existing methods often struggle to maintain agent positivity under privacy constraints.

    Purpose of the Study:

    • To develop a differentially private average consensus algorithm for MASs with positive agents.
    • To introduce a novel randomized mechanism that preserves agent state positivity and randomness.
    • To ensure the (ϵ,δ)-differential privacy of the MAS.

    Main Methods:

    • A novel randomized mechanism using non-decaying positive multiplicative truncated Gaussian noises.
    • Development of a time-varying controller for mean-square positive average consensus.
    • Derivation of the privacy budget and evaluation of convergence accuracy.

    Main Results:

    • The proposed mechanism successfully maintains the positivity and randomness of agent states.
    • The time-varying controller achieves mean-square positive average consensus with evaluated convergence accuracy.
    • The (ϵ,δ)-differential privacy of the MAS is preserved by the introduced mechanism.

    Conclusions:

    • The novel randomized mechanism and time-varying controller effectively enable differentially private average consensus in MASs.
    • The approach guarantees both privacy preservation and accurate consensus achievement for positive agents.
    • Numerical examples validate the efficacy of the proposed privacy-preserving consensus strategy.