Utilitarian Distributed Constraint Optimization Problems

TL;DR

This paper introduces UDCOP, a novel approach that incorporates privacy requirements into distributed constraint optimization, improving privacy preservation without sacrificing solution quality.

Contribution

It proposes the UDCOP framework that integrates privacy considerations into the search process of DCOP algorithms, with extensions to enhance privacy preservation.

Findings

Stochastic algorithms can be adapted to better preserve privacy.

Extensions to algorithms significantly reduce privacy loss.

Solution quality remains high despite privacy-focused modifications.

Abstract

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.