A Privacy Preserving Collusion Secure DCOP Algorithm
This addresses a critical limitation in existing privacy-preserving DCOP algorithms, which assume no collusion, making it an essential step for enhancing security in multi-agent systems.
The paper tackles the problem of collusion in privacy-preserving Distributed Constraint Optimization Problems (DCOPs) by proposing PC-SyncBB, the first algorithm immune to coalitions under an honest majority assumption, showing that achieving such security is feasible.
In recent years, several studies proposed privacy-preserving algorithms for solving Distributed Constraint Optimization Problems (DCOPs). All of those studies assumed that agents do not collude. In this study we propose the first privacy-preserving DCOP algorithm that is immune to coalitions, under the assumption of honest majority. Our algorithm -- PC-SyncBB -- is based on the classical Branch and Bound DCOP algorithm. It offers constraint, topology and decision privacy. We evaluate its performance on different benchmarks, problem sizes, and constraint densities. We show that achieving security against coalitions is feasible. As all existing privacy-preserving DCOP algorithms base their security on assuming solitary conduct of the agents, we view this study as an essential first step towards lifting this potentially harmful assumption in all those algorithms.