Consensus with Preserved Privacy against Neighbor Collusion
This work addresses the problem of privacy and security in distributed consensus for networks of agents, providing a robust solution against neighbor collusion and eavesdropping.
This paper introduces a privacy-preserving algorithm for the average consensus problem, enabling a network of agents to agree on their states without revealing individual states until consensus is achieved. The algorithm is robust against collusion among any number of neighbors, including all neighbors, and also protects the network consensus procedure from eavesdropping.
This paper proposes a privacy-preserving algorithm to solve the average consensus problem based on Shamir's secret sharing scheme, in which a network of agents reach an agreement on their states without exposing their individual state until an agreement is reached. Unlike other methods, the proposed algorithm renders the network resistant to the collusion of any given number of neighbors (even with all neighbors' colluding). Another virtue of this work is that such a method can protect the network consensus procedure from eavesdropping.