TY - GEN
T1 - A privacy preserving collusion secure DCOP algorithm
AU - Tassa, Tamir
AU - Grinshpoun, Tal
AU - Yanai, Avishay
N1 - Publisher Copyright:
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In recent years, several studies proposed privacy-preserving algorithms for solving Distributed Constraint Optimization Problems (DCOPs). All of those studies assumed that agents do not collude. In this study we propose the first privacy-preserving DCOP algorithm that is immune to coalitions, under the assumption of honest majority. Our algorithm - PC-SyncBB - is based on the classical Branch and Bound DCOP algorithm. It offers constraint, topology and decision privacy. We evaluate its performance on different benchmarks, problem sizes, and constraint densities. We show that achieving security against coalitions is feasible. As all existing privacy-preserving DCOP algorithms base their security on assuming solitary conduct of the agents, we view this study as an essential first step towards lifting this potentially harmful assumption in all those algorithms.
AB - In recent years, several studies proposed privacy-preserving algorithms for solving Distributed Constraint Optimization Problems (DCOPs). All of those studies assumed that agents do not collude. In this study we propose the first privacy-preserving DCOP algorithm that is immune to coalitions, under the assumption of honest majority. Our algorithm - PC-SyncBB - is based on the classical Branch and Bound DCOP algorithm. It offers constraint, topology and decision privacy. We evaluate its performance on different benchmarks, problem sizes, and constraint densities. We show that achieving security against coalitions is feasible. As all existing privacy-preserving DCOP algorithms base their security on assuming solitary conduct of the agents, we view this study as an essential first step towards lifting this potentially harmful assumption in all those algorithms.
UR - http://www.scopus.com/inward/record.url?scp=85074923498&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2019/663
DO - 10.24963/ijcai.2019/663
M3 - Conference contribution
AN - SCOPUS:85074923498
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 4774
EP - 4780
BT - Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
A2 - Kraus, Sarit
PB - International Joint Conferences on Artificial Intelligence
T2 - 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Y2 - 10 August 2019 through 16 August 2019
ER -