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Differentially Private Consensus With Quantized Communication.

Lan Gao, Shaojiang Deng, Wei Ren

IEEE Transactions on Cybernetics

|March 8, 2019

Summary

This summary is machine-generated.

This study introduces a new differentially private consensus algorithm for multiagent networks using quantized communication. The method ensures secure state agreement even with limited data transmission, protecting participant information.

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Area of Science:

Control Systems
Network Security
Information Theory

Background:

Multiagent systems require consensus protocols for coordinated behavior.
Quantized communication limits state information transmission in networks.
Differential privacy is crucial for protecting individual agent data.

Purpose of the Study:

To develop a differentially private consensus algorithm for quantized communication environments.
To analyze the convergence and accuracy of the proposed algorithm.
To ensure data privacy for individual agents and the entire network.

Main Methods:

Extending the differentially private consensus model to incorporate quantized communication.
Proposing a differentially private communication algorithm using bounded quantizers.
Conducting convergence analysis for unbounded quantizers and statistical analysis for bounded quantizers.
Performing differential privacy analysis for agents and the network.

Main Results:

The proposed algorithm achieves consensus while protecting agent states under quantized communication.
Convergence analysis confirms the model's sufficiency and establishes convergence conditions.
Statistical analysis shows bounded quantizers can remain unsaturated with high probability.
Convergent accuracy analysis indicates agreement converges near the initial state average, with the expectation matching it exactly.

Conclusions:

The developed algorithm and theoretical results are validated by simulations.
The study demonstrates effective differentially private consensus in quantized multiagent networks.
A link between dynamic encoding/decoding schemes and differential privacy mechanisms is established.