Privacy-Preserving Average Consensus — A Lightweight Method

The average consensus problem is a crucial research topic in distributed systems. The realization of average consensus relies on explicit state information exchanging among neighboring nodes, which is unacceptable in cases where the state is private or contains sensitive information. Hence, it is essential to adopt privacy-preserving algorithms to safeguard the state information during consensus procedures. However, existing privacy-preserving average consensus approaches which guarantee convergence to the accurate desired value inevitably improve the topological complexity, increase computational cost, and reduce the convergence speed. In this paper, we propose a lightweight privacy-preserving average consensus method. The key idea of our method is a state conceal mechanism and a state-decomposition-selection algorithm. By selecting partial nodes in the network for state decomposition to achieve privacy preservation and increasing the step size in the consensus protocol, our method provides faster convergence speed and lower computational cost when compared with the algorithm in the seminal paper [1]. Theoretical analysis and numerical simulations demonstrate the effectiveness of our method.