Privacy has traditionally been a major motivation of distributed problem solving. One popular approach to enable privacy in distributed environments is to implement complex cryptographic protocols. In this paper, we propose a different, orthogonal approach, which is to control the quality and the quantity of publicized data. We consider the Open Constraint Programming model and focus on algorithms that solve Distributed Constraint Optimization Problems (DCOPs) using a local search approach. Two such popular algorithms exist to find good solutions to DCOP: DSA and GDBA. In this paper, we propose DSAB, a new algorithm that merges ideas from both algorithms to allow extensive handling of constraint privacy. We also study how algorithms behave when solving Utilitarian DCOPs, where utilitarian agents want to reach an agreement while reducing the privacy loss. We experimentally study how the utilitarian approach impacts the quality of the solution and of publicized data.

传统上，隐私是解决分布式问题的主要动机。在分布式环境中启用隐私的一种流行方法是实现复杂的密码协议。在本文中，我们提出了一种不同的正交方法，即控制公开数据的质量和数量。我们考虑开放约束规划模型，重点研究使用局部搜索方法解决分布式约束优化问题（DCOP）的算法。存在两种这样的流行算法来找到DCOP的良好解决方案：DSA和GDBA。在本文中，我们提出了一种新的算法DSAB，该算法将两种算法的思想融合在一起，以实现对约束隐私的广泛处理。我们还研究了算法在解决功利性DCOP时的行为，功利代理想达成协议，同时减少隐私损失的地方。我们通过实验研究了功利主义方法如何影响解决方案和公开数据的质量。