Experimental analysis of privacy loss in DCOP algorithms (short paper)


Rachel Greenstadt, Jonathan P. Pearce, Emma Bowring, and Milind Tambe. 2006. “Experimental analysis of privacy loss in DCOP algorithms (short paper).” In Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS).


Distributed Constraint Optimization (DCOP) is rapidly emerging as a prominent technique for multiagent coordination. Unfortunately, rigorous quantitative evaluations of privacy loss in DCOP algorithms have been lacking despite the fact that agent privacy is a key motivation for applying DCOPs in many applications. Recently, Maheswaran et al. [3, 4] introduced a framework for quantitative evaluations of privacy in DCOP algorithms, showing that early DCOP algorithms lose more privacy than purely centralized approaches and questioning the motivation for applying DCOPs. Do state-of-the art DCOP algorithms suffer from a similar shortcoming? This paper answers that question by investigating the most efficient DCOP algorithms, including both DPOP and ADOPT.
See also: 2006