P-SyncBB: A privacy preserving branch and bound DCOP algorithm

Tal Grinshpoun, Tamir Tassa

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


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.

Original languageEnglish
Pages (from-to)621-660
Number of pages40
JournalJournal of Artificial Intelligence Research
StatePublished - 1 Dec 2016


Dive into the research topics of 'P-SyncBB: A privacy preserving branch and bound DCOP algorithm'. Together they form a unique fingerprint.

Cite this