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Average consensus protocols emerge with a central role in distributed systemsand decision-making such as distributed information fusion, distributedoptimization, distributed estimation, and control. A key advantage of theseprotocols is that agents exchange and reveal their state information only totheir neighbors. Yet, it can raise privacy concerns in situations where theagents' states contain sensitive information. In this paper, we propose a novel(noiseless) privacy preserving distributed algorithms for multi-agent systemsto reach an average consensus. The main idea of the algorithms is that eachagent runs a (small) network with a crafted structure and dynamics to form anetwork of networks (i.e., the connection between the newly created networksand their interconnections respecting the initial network connections).Together with a re-weighting of the dynamic parameters dictating theinter-agent dynamics and the initial states, we show that it is possible toensure that the value of each node converges to the consensus value of theoriginal network. Furthermore, we show that, under mild assumptions, it ispossible to craft the dynamics such that the design can be achieved in adistributed fashion. Finally, we illustrate the proposed algorithm withexamples.

Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation