Observer-based Differentially Private Consensus for Linear Multi-agent Systems

TL;DR

This paper develops an observer-based framework for achieving differentially private consensus in linear multi-agent systems, ensuring output privacy while maintaining convergence and providing design guidelines.

Contribution

It introduces a novel joint design method for observer gains, control gains, and noise to ensure differential privacy and consensus in linear MASs, including both full-order and reduced-order observers.

Findings

Achieves mean square and almost sure consensus with Laplace noise

Validates the separation principle under privacy-preserving conditions

Provides convergence rate and privacy level guarantees

Abstract

This paper investigates the differentially private consensus problem for general linear multi-agent systems (MASs) based on output feedback protocols. To protect the output information, which is considered private data and may be at high risk of exposure, Laplace noise is added to the information exchange. The conditions for achieving mean square and almost sure consensus in observer-based MASs are established using the backstepping method and the convergence theory for nonnegative almost supermartingales. It is shown that the separation principle remains valid for the consensus problem of linear MASs with decaying Laplace noise. Furthermore, the convergence rate is provided. Then, a joint design framework is developed for state estimation gain, feedback control gain, and noise to ensure the preservation of \epsilon-differential privacy. The output information of each agent is shown to be protected at every time step. Finally, sufficient conditions are established for simultaneously achieving consensus and preserving differential privacy for linear MASs utilizing both full-order and reduced-order observers. Meanwhile, an \epsilon*-differentially private consensus is achieved to meet the desired privacy level. Two simulation examples are provided to validate the theoretical results.