Protecting Privacy in Multi-agent Optimization
Many real-life optimization problems involve multiple entities, or agents (individuals, companies...), with their own private constraints and preferences, communicating with each other in order to find a solution that maximizes the overall public satisfaction. In Artificial Intelligence, the field of Distributed Constraint Optimization (DCOP) has been addressing such multi-agent optimization problems, through distributed message-passing algorithms such as the DPOP algorithm. However, the research in DCOP has been neglecting the privacy of the information exchanged by the agents during the computing of the solution, which is critical to many real-life problems. While agents are willing to cooperate with each other to produce an optimal solution, they are most often reluctant to reveal their private constraints and preferences to other, which hinders this cooperation. The goal of this project was to implement, test, and evaluate a secured version of DPOP, P-DPOP. P-DPOP provides strong agent privacy and topology privacy by randomization, constraints privacy and limited decision privacy by obfuscation. The algorithm was implemented in Java, as part of the open-source FRODO platform for DCOP.