False-Name-Proofness in Social Networks

  • Vincent Conitzer
  • Nicole Immorlica
  • Joshua Letchford
  • Kamesh Munagala
  • Liad Wagman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6484)

Abstract

In mechanism design, the goal is to create rules for making a decision based on the preferences of multiple parties (agents), while taking into account that agents may behave strategically. An emerging phenomenon is to run such mechanisms on a social network; for example, Facebook recently allowed its users to vote on its future terms of use. One significant complication for such mechanisms is that it may be possible for a user to participate multiple times by creating multiple identities. Prior work has investigated the design of false-name-proof mechanisms, which guarantee that there is no incentive to use additional identifiers. Arguably, this work has produced mostly negative results. In this paper, we show that it is in fact possible to create good mechanisms that are robust to false-name-manipulation, by taking the social network structure into account. The basic idea is to exclude agents that are separated from trusted nodes by small vertex cuts. We provide key results on the correctness, optimality, and computational tractability of this approach.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Albert, R., Barabási, A.–L.: Statistical mechanics of complex networks (2001)Google Scholar
  2. 2.
    Andersen, R., Borgs, C., Chayes, J., Hopcroft, J., Mirrokni, V., Teng, S.: Local computation of PageRank contributions. In: WAW (2007)Google Scholar
  3. 3.
    Andersen, R., Borgs, C., Chayes, J.T., Hopcroft, J.E., Jain, K., Mirrokni, V.S., Teng, S.-H.: Robust PageRank and locally computable spam detection features. In: AIRWeb, pp. 69–76 (2008)Google Scholar
  4. 4.
    Conitzer, V.: Limited verification of identities to induce false-name-proofness. In: TARK, Brussels, Belgium, pp. 102–111 (2007)Google Scholar
  5. 5.
    Conitzer, V.: Anonymity-proof voting rules. In: Papadimitriou, C., Zhang, S. (eds.) WINE 2008. LNCS, vol. 5385, pp. 295–306. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Gyöngyi, Z., Berkhin, P., Garcia-Molina, H., Pedersen, J.: Link spam detection based on mass estimation. In: Proceedings of VLDB. ACM, New York (2006)Google Scholar
  7. 7.
    Gyöngyi, Z., Garcia-Molina, H., Pedersen, J.: Combating web spam with TrustRank. In: Proceedings of VLDB, pp. 576–587. Morgan Kaufmann, San Francisco (2004)Google Scholar
  8. 8.
    Iwasaki, A., Conitzer, V., Omori, Y., Sakurai, Y., Todo, T., Guo, M., Yokoo, M.: Worst-case efficiency ratio in false-name-proof combinatorial auction mechanisms. In: Proceedings of AAMAS, Toronto, Canada, pp. 633–640 (2010)Google Scholar
  9. 9.
    Kleinberg, J.M.: Navigation in a small world. Nature (2000)Google Scholar
  10. 10.
    Luce, D.R., Raiffa, H.: Games and decisions: introduction and critical survey, New York (1957)Google Scholar
  11. 11.
    Menger, K.: Zur allgemeinen Kurventheorie. Fund. Math. 10, 96–115 (1927)CrossRefMATHGoogle Scholar
  12. 12.
    Nagamochi, H., Ishii, T., Ito, H.: Minimum cost source location problem with vertex-connectivity requirements in digraphs. Information Processing Letters 80(6), 287–293 (2001)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Raj, R., Krishnan, V.: Web spam detection with anti-trust rank. In: AIRWeb, pp. 381–389 (2006)Google Scholar
  14. 14.
    Wagman, L., Conitzer, V.: Optimal false-name-proof voting rules with costly voting. In: Proceedings of AAAI, Chicago, IL, USA, pp. 190–195 (2008)Google Scholar
  15. 15.
    Yokoo, M., Sakurai, Y., Matsubara, S.: Robust combinatorial auction protocol against false-name bids. Artificial Intelligence 130(2), 167–181 (2001)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Yokoo, M., Sakurai, Y., Matsubara, S.: The effect of false-name bids in combinatorial auctions: New fraud in Internet auctions. GEB 46(1), 174–188 (2004)MathSciNetMATHGoogle Scholar
  17. 17.
    Yu, H., Gibbons, P.B., Kaminsky, M., Xiao, F.: SybilLimit: A near-optimal social network defense against sybil attacks. ToN 18(3), 885–898 (2010)Google Scholar
  18. 18.
    Yu, H., Kaminsky, M., Gibbons, P.B., Flaxman, A.: SybilGuard: Defending against sybil attacks via social networks. ToN 16(3), 576–589 (2008)Google Scholar
  19. 19.
    Zuckerberg, M.: Voting begins on governing the Facebook site (2009), http://blog.facebook.com/blog.php?post=76815337130

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Vincent Conitzer
    • 1
  • Nicole Immorlica
    • 2
  • Joshua Letchford
    • 1
  • Kamesh Munagala
    • 1
  • Liad Wagman
    • 3
  1. 1.Duke UniversityUSA
  2. 2.Northwestern UniversityUSA
  3. 3.Illinios Institute of TechnologyUSA

Personalised recommendations