Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation
This addresses privacy concerns for sensitive state information in distributed systems like information fusion and optimization, though it appears incremental as it builds on existing consensus methods.
The paper tackles the problem of privacy leakage in multi-agent average consensus protocols by proposing a noiseless privacy-preserving algorithm that uses network augmentation and re-weighting to ensure convergence to the original consensus value, with examples provided for illustration.
Average consensus protocols emerge with a central role in distributed systems and decision-making such as distributed information fusion, distributed optimization, distributed estimation, and control. A key advantage of these protocols is that agents exchange and reveal their state information only to their neighbors. Yet, it can raise privacy concerns in situations where the agents' states contain sensitive information. In this paper, we propose a novel (noiseless) privacy preserving distributed algorithms for multi-agent systems to reach an average consensus. The main idea of the algorithms is that each agent runs a (small) network with a crafted structure and dynamics to form a network of networks (i.e., the connection between the newly created networks and their interconnections respecting the initial network connections). Together with a re-weighting of the dynamic parameters dictating the inter-agent dynamics and the initial states, we show that it is possible to ensure that the value of each node converges to the consensus value of the original network. Furthermore, we show that, under mild assumptions, it is possible to craft the dynamics such that the design can be achieved in a distributed fashion. Finally, we illustrate the proposed algorithm with examples.