Privacy-Preserved Consensus Control for Second-Order Multiagent Systems: a Position and Velocity Simultaneous...

Area of Science:

• Control Theory
• Cybersecurity
• Robotics

Background:

• Multiagent systems (MASs) are increasingly used in various applications.
• Ensuring privacy of individual agent states in MASs is a critical challenge.
• Adversaries may attempt to infer private information like initial states.

Purpose of the Study:

• To develop a privacy preservation algorithm for consensus control in second-order integrator MASs.
• To protect the initial position and velocity of legitimate agents from adversaries.
• To analyze the privacy performance and identify conditions for privacy leakage.

Main Methods:

• A novel privacy preservation algorithm based on simultaneous perturbation of position and velocity.
• A two-phase approach: collaborative scrambling for privacy and convergence for consensus.
• System-theoretic framework for analyzing privacy performance based on indistinguishability.

Main Results:

• The proposed algorithm effectively preserves privacy against internal and external adversaries under certain conditions.
• An internal adversary cannot infer private states if the agent has legitimate neighbors.
• Privacy is compromised only if an agent exclusively connects to an internal adversary bidirectionally.
• External adversaries cannot infer privacy if scrambling gain parameters are inaccessible.

Conclusions:

• The developed algorithm offers a robust solution for privacy preservation in MAS consensus control.
• The system-theoretic analysis provides clear conditions for privacy guarantees.
• Simulations validate the effectiveness of the proposed privacy-preserving approach.