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International Symposium on Cyber Security, Cryptology, and Machine Learning

CSCML 2022: Cyber Security, Cryptology, and Machine Learning pp 487–498Cite as

Privacy Preserving DCOP Solving by Mediation

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13301))

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.

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Notes

  1. 1.

    We make the standard assumption that the number of variables equals the number of agents, and that each variable is held by a distinct single agent, see e.g. [11, 12].

References

  1. Farinelli, A., Rogers, A., Jennings, N.R.: Decentralised coordination of low-power embedded devices using the max-sum algorithm. In: AAMAS, pp. 639–646 (2008)

    Google Scholar 

  2. Fioretto, F., Pontelli, E., Yeoh, W.: Distributed constraint optimization problems and applications: a survey. J. Artif. Intell. Res. 61, 623–698 (2018)

    Article  MathSciNet  Google Scholar 

  3. Grinshpoun, T., Tassa, T.: P-SyncBB: a privacy preserving branch and bound DCOP algorithm. J. Artif. Intell. Res. 57, 621–660 (2016)

    Article  MathSciNet  Google Scholar 

  4. Grinshpoun, T., Tassa, T., Levit, V., Zivan, R.: Privacy preserving region optimal algorithms for symmetric and asymmetric DCOPs. Artif. Intell. 266, 27–50 (2019)

    Article  MathSciNet  Google Scholar 

  5. Hirayama, K., Yokoo, M.: Distributed partial constraint satisfaction problem. In: CP, pp. 222–236 (1997)

    Google Scholar 

  6. Katagishi, H., Pearce, J.P.: Kopt: Distributed DCOP algorithm for arbitrary k-optima with monotonically increasing utility. In: DCR (2007)

    Google Scholar 

  7. Kiekintveld, C., Yin, Z., Kumar, A., Tambe, M.: Asynchronous algorithms for approximate distributed constraint optimization with quality bounds. In: AAMAS, pp. 133–140 (2010)

    Google Scholar 

  8. Kogan, P.: Privacy Preserving Solution of DCOPs by Mediation. Master’s thesis, supervised by Tassa, T. and Grinshpoun, T., The Open University of Israel (2022). https://www.openu.ac.il/Lists/MediaServer_Documents/PersonalSites/TamirTassa/MD_Max_Sum.pdf

  9. Léauté, T., Faltings, B.: Protecting privacy through distributed computation in multi-agent decision making. J. Artif. Intell. Res. 47, 649–695 (2013)

    Article  MathSciNet  Google Scholar 

  10. Lutati, B., Gontmakher, I., Lando, M., Netzer, A., Meisels, A., Grubshtein, A.: AgentZero: a framework for simulating and evaluating multi-agent algorithms. In: Agent-Oriented Software Engineering, pp. 309–327 (2014)

    Google Scholar 

  11. Modi, P.J., Shen, W.M., Tambe, M., Yokoo, M.: ADOPT: asynchronous distributed constraint optimization with quality guarantees. Artif. Intell. 161, 149–180 (2005)

    Article  MathSciNet  Google Scholar 

  12. Petcu, A., Faltings, B.: A scalable method for multiagent constraint optimization. In: IJCAI, pp. 266–271 (2005)

    Google Scholar 

  13. Shamir, A.: How to share a secret. Commun. ACM 22(11), 612–613 (1979)

    Article  MathSciNet  Google Scholar 

  14. Tassa, T., Grinshpoun, T., Yanai, A.: PC-SyncBB: a privacy preserving collusion secure DCOP algorithm. Artif. Intell. 297, 103501 (2021)

    Google Scholar 

  15. Tassa, T., Grinshpoun, T., Zivan, R.: Privacy preserving implementation of the Max-Sum algorithm and its variants. J. Artif. Intell. Res. 59, 311–349, Article no. 103501 (2017)

    Google Scholar 

  16. Yao, A.C.: Protocols for secure computation. In: FOCS, pp. 160–164 (1982)

    Google Scholar 

  17. Zivan, R., Okamoto, S., Peled, H.: Explorative anytime local search for distributed constraint optimization. Artif. Intell. 212, 1–26 (2014)

    Article  MathSciNet  Google Scholar 

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Acknowledgments

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.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, The Open University of Israel, Ra’anana, Israel

    Pablo Kogan & Tamir Tassa

  2. Department of Industrial Engineering and Management, Ariel University, Ariel, Israel

    Tal Grinshpoun

  3. Ariel Cyber Innovation Center, Ariel University, Ariel, Israel

    Pablo Kogan & Tal Grinshpoun

Authors
  1. Pablo Kogan
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  2. Tamir Tassa
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  3. Tal Grinshpoun
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Corresponding author

Correspondence to Tamir Tassa .

Editor information

Editors and Affiliations

  1. Ben-Gurion University of the Negev, Be’er Sheva, Israel

    Shlomi Dolev

  2. University of Maryland, College Park, MD, USA

    Jonathan Katz

  3. Ben-Gurion University of the Negev, Be’er Sheva, Israel

    Amnon Meisels

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Kogan, P., Tassa, T., Grinshpoun, T. (2022). Privacy Preserving DCOP Solving by Mediation. In: Dolev, S., Katz, J., Meisels, A. (eds) Cyber Security, Cryptology, and Machine Learning. CSCML 2022. Lecture Notes in Computer Science, vol 13301. Springer, Cham. https://doi.org/10.1007/978-3-031-07689-3_34

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  • DOI: https://doi.org/10.1007/978-3-031-07689-3_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07688-6

  • Online ISBN: 978-3-031-07689-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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