Privacy Preserving DCOP Solving by Mediation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageAmerican English
Title of host publicationCyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings
EditorsShlomi Dolev, Amnon Meisels, Jonathan Katz
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031076886
StatePublished - 1 Jan 2022
Event6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022 - Beer Sheva, Israel
Duration: 30 Jun 20221 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13301 LNCS


Conference6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
CityBeer Sheva


  • DCOP
  • Max-Sum
  • Mediated computing
  • Multiparty computation
  • Privacy

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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