文章基本信息

标题：P-SyncBB: A Privacy Preserving Branch and Bound DCOP Algorithm
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作者：Tal Grinshpoun ; Tamir Tassa
期刊名称：Journal of Artificial Intelligence Research
印刷版ISSN：1076-9757
出版年度：2016
卷号：57
页码：621-660
出版社：American Association of Artificial
摘要：Distributed constraint optimization problems enable the representation of many combinatorial problems that are distributed by nature. An important motivation for such problems is to preserve the privacy of the participating agents during the solving process. The present paper introduces a novel privacy-preserving branch and bound algorithm for this purpose. The proposed algorithm, P-SyncBB, preserves constraint, topology and decision privacy. The algorithm requires secure solutions to several multi-party computation problems. Consequently, appropriate novel secure protocols are devised and analyzed. An extensive experimental evaluation on different benchmarks, problem sizes, and constraint densities shows that P-SyncBB exhibits superior performance to other privacy-preserving complete DCOP algorithms.