TY - GEN
T1 - Privacy Preserving DCOP Solving by Mediation
AU - Kogan, Pablo
AU - Tassa, Tamir
AU - Grinshpoun, Tal
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - In this study we propose a new paradigm for solving DCOPs, whereby the agents delegate the computational task to a set of external mediators who perform the computations for them in an oblivious manner, without getting access neither to the problem inputs nor to its outputs. Specifically, we propose MD-Max-Sum, a mediated implementation of the Max-Sum algorithm. MD-Max-Sum offers topology, constraint, and decision privacy, as well as partial agent privacy. Moreover, MD-Max-Sum is collusion-secure, as long as the set of mediators has an honest majority. We evaluate the performance of MD-Max-Sum on different benchmarks. In particular, we compare its performance to PC-SyncBB, the only privacy-preserving DCOP algorithm to date that is collusion-secure, and show the significant advantages of MD-Max-Sum in terms of runtime.
AB - In this study we propose a new paradigm for solving DCOPs, whereby the agents delegate the computational task to a set of external mediators who perform the computations for them in an oblivious manner, without getting access neither to the problem inputs nor to its outputs. Specifically, we propose MD-Max-Sum, a mediated implementation of the Max-Sum algorithm. MD-Max-Sum offers topology, constraint, and decision privacy, as well as partial agent privacy. Moreover, MD-Max-Sum is collusion-secure, as long as the set of mediators has an honest majority. We evaluate the performance of MD-Max-Sum on different benchmarks. In particular, we compare its performance to PC-SyncBB, the only privacy-preserving DCOP algorithm to date that is collusion-secure, and show the significant advantages of MD-Max-Sum in terms of runtime.
KW - DCOP
KW - Max-Sum
KW - Mediated computing
KW - Multiparty computation
KW - Privacy
UR - http://www.scopus.com/inward/record.url?scp=85134187287&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-07689-3_34
DO - 10.1007/978-3-031-07689-3_34
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AN - SCOPUS:85134187287
SN - 9783031076886
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 487
EP - 498
BT - Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings
A2 - Dolev, Shlomi
A2 - Meisels, Amnon
A2 - Katz, Jonathan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
Y2 - 30 June 2022 through 1 July 2022
ER -