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.
|Title of host publication||Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings|
|Editors||Shlomi Dolev, Amnon Meisels, Jonathan Katz|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||12|
|State||Published - 2022|
|Event||6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022 - Beer Sheva, Israel|
Duration: 30 Jun 2022 → 1 Jul 2022
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022|
|Period||30/06/22 → 1/07/22|
Bibliographical noteFunding Information:
This work was partially supported by the Ariel Cyber Innovation Center in conjunction with the Israel National Cyber Directorate in the Prime Minister’s Office.
© 2022, Springer Nature Switzerland AG.
- Mediated computing
- Multiparty computation