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

Tal Grinshpoun, Tamir Tassa

פרסום מחקרי: פרסום בכתב עתמאמרביקורת עמיתים


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.

שפה מקוריתאנגלית
עמודים (מ-עד)621-660
מספר עמודים40
כתב עתJournal of Artificial Intelligence Research
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 1 דצמ׳ 2016

הערה ביבליוגרפית

Publisher Copyright:
© 2016 AI Access Foundation.

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