Privacy Preserving DCOP Solving by Mediation

Pablo Kogan; Tamir Tassa; Tal Grinshpoun

doi:10.1007/978-3-031-07689-3_34

Privacy Preserving DCOP Solving by Mediation

Pablo Kogan, Tamir Tassa, Tal Grinshpoun

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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 language	English
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
Pages	487-498
Number of pages	12
ISBN (Print)	9783031076886
DOIs	https://doi.org/10.1007/978-3-031-07689-3_34
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

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13301 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022
Country/Territory	Israel
City	Beer Sheva
Period	30/06/22 → 1/07/22

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

DCOP
Max-Sum
Mediated computing
Multiparty computation
Privacy

Access to Document

10.1007/978-3-031-07689-3_34

Cite this

Kogan, P., Tassa, T., & Grinshpoun, T. (2022). Privacy Preserving DCOP Solving by Mediation. In S. Dolev, A. Meisels, & J. Katz (Eds.), Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings (pp. 487-498). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13301 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-07689-3_34

Kogan, Pablo ; Tassa, Tamir ; Grinshpoun, Tal. / Privacy Preserving DCOP Solving by Mediation. Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings. editor / Shlomi Dolev ; Amnon Meisels ; Jonathan Katz. Springer Science and Business Media Deutschland GmbH, 2022. pp. 487-498 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Kogan, P, Tassa, T & Grinshpoun, T 2022, Privacy Preserving DCOP Solving by Mediation. in S Dolev, A Meisels & J Katz (eds), Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13301 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 487-498, 6th International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2022, Beer Sheva, Israel, 30/06/22. https://doi.org/10.1007/978-3-031-07689-3_34

Privacy Preserving DCOP Solving by Mediation. / Kogan, Pablo; Tassa, Tamir; Grinshpoun, Tal.
Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings. ed. / Shlomi Dolev; Amnon Meisels; Jonathan Katz. Springer Science and Business Media Deutschland GmbH, 2022. p. 487-498 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13301 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Kogan P, Tassa T, Grinshpoun T. Privacy Preserving DCOP Solving by Mediation. In Dolev S, Meisels A, Katz J, editors, Cyber Security, Cryptology, and Machine Learning - 6th International Symposium, CSCML 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 487-498. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-07689-3_34