Secure Average Consensus Algorithm with Dynamics-Based Privacy Preservation for Unbalanced Digraphs

The core message of this article is to develop a novel privacy-preserving average consensus algorithm for unbalanced digraphs. The algorithm carefully embeds randomness in mixing weights and introduces an auxiliary parameter to mask the state-update rule in the initial iterations, while exploiting the intrinsic robustness of consensus dynamics to guarantee the exact average consensus.