Utilitarian Distributed Constraint Optimization Problems
  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.
