Utilitarian Distributed Constraint Optimization Problems
This work addresses privacy concerns in distributed optimization for agents or systems, but it appears incremental as it builds on existing DCOP methods with heuristic modifications.
The paper tackles the challenge of preserving privacy in Distributed Constraint Optimization Problems (DCOPs) by introducing Utilitarian DCOP (UDCOP), which integrates privacy requirements into the search process, resulting in significantly reduced privacy loss without major degradation in solution quality.
Privacy has been a major motivation for distributed problem optimization. However, even though several methods have been proposed to evaluate it, none of them is widely used. The Distributed Constraint Optimization Problem (DCOP) is a fundamental model used to approach various families of distributed problems. As privacy loss does not occur when a solution is accepted, but when it is proposed, privacy requirements cannot be interpreted as a criteria of the objective function of the DCOP. Here we approach the problem by letting both the optimized costs found in DCOPs and the privacy requirements guide the agents' exploration of the search space. We introduce Utilitarian Distributed Constraint Optimization Problem (UDCOP) where the costs and the privacy requirements are used as parameters to a heuristic modifying the search process. Common stochastic algorithms for decentralized constraint optimization problems are evaluated here according to how well they preserve privacy. Further, we propose some extensions where these solvers modify their search process to take into account their privacy requirements, succeeding in significantly reducing their privacy loss without significant degradation of the solution quality.