Experimental analysis of privacy loss in DCOP algorithms
Experimental analysis of privacy loss in DCOP algorithms
复制标题
DCOP算法中隐私损失的实验分析
DOI：
10.1145/1160633.1160899
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Milind Tambe
中科院分区：
文献类型：
--
作者：
R. Greenstadt;J. Pearce;E. Bowring;Milind Tambe
分布式约束优化（DCOP）是一种新兴的多智能体协调技术。不幸的是，严格的定量评估的隐私损失DCOP算法一直缺乏，尽管代理隐私是一个关键的动机，在许多应用程序中应用DCOP。最近，Maheswaran等人。[3，4]介绍了一个在DCOP算法中对隐私进行定量评估的框架，表明早期的DCOP算法比纯粹的集中式方法失去了更多的隐私，并质疑应用DCOP的动机。最先进的DCOP算法也有类似的缺点吗？本文回答了这个问题，调查最有效的DCOP算法，包括DPOP和ADOPT。
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.
影响因子：
14.4
作者：
Modi, PJ;Shen, WM;Yokoo, M
通讯作者：
Yokoo, M