Encyclopedia of Algorithms

2016 Edition
| Editors: Ming-Yang Kao

Privacy Preserving Auction

  • Zhiyi HuangEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-2864-4_791

Years and Authors of Summarized Original Work

  • 2012; Huang, Kannan

  • 2014; Hsu, Huang, Roth, Roughgarden, Wu

Problem Definition

Let there be n agents and a set of feasible outcomes Ω. For concreteness, readers may think of Ω as the set of allocations of m items to n agents. Each agent has a private value function vi : Ω ↦ [0, 1] over feasible outcomes. We focus on direct revelation mechanisms, which first let each agent i report a value function \(\bar{v}_{i}\)

Keywords

Differential privacy Mechanism design 
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Recommended Reading

  1. 1.
    Chen Y, Chong S, Kash IA, Moran T, Vadhan S (2013) Truthful mechanisms for agents that value privacy. In: 14th conference on electronic commerce. ACM, New York, pp 215–232Google Scholar
  2. 2.
    Clarke EH (1971) Multipart pricing of public goods. Public Choice 11(1):17–33CrossRefGoogle Scholar
  3. 3.
    Dwork C, McSherry F, Nissim K, Smith A (2006) Calibrating noise to sensitivity in private data analysis. In: Theory of cryptography. Springer, Berlin/Heidelberg, pp 265–284CrossRefGoogle Scholar
  4. 4.
    Feige U (2009) On maximizing welfare when utility functions are subadditive. SIAM J Comput 39(1):122–142MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Groves T (1973) Incentives in teams. Econom J Econom Soc 41:617–631MathSciNetzbMATHGoogle Scholar
  6. 6.
    Hsu J, Huang Z, Roth A, Roughgarden T, Wu SZ (2014) Private matchings and allocations. In: 46th annual symposium on theory of computing (STOC). ACM, New YorkGoogle Scholar
  7. 7.
    Huang Z, Kannan S (2012) The exponential mechanism for social welfare: private, truthful, and nearly optimal. In: 53rd annual symposium on foundations of computer science (FOCS). IEEE, Washington, DC, pp 140–149Google Scholar
  8. 8.
    Kearns M, Pai M, Roth A, Ullman J (2014) Mechanism design in large games: incentives and privacy. In: 5th conference on innovations in theoretical computer science. ACM, New York, pp 403–410CrossRefGoogle Scholar
  9. 9.
    Kelso AS, Crawford VP (1982) Job matching, coalition formation, and gross substitutes. Econom J Econom Soc 50:1483–1504MathSciNetzbMATHGoogle Scholar
  10. 10.
    McSherry F, Talwar K (2007) Mechanism design via differential privacy. In: 48th annual symposium on foundations of computer science (FOCS). IEEE, pp 94–103Google Scholar
  11. 11.
    Nisan N, Roughgarden T, Tardos E, Vazirani VV (2007) Algorithmic game theory. Cambridge University Press, Cambridge/New YorkzbMATHCrossRefGoogle Scholar
  12. 12.
    Nissim K, Orlandi C, Smorodinsky R (2012) Privacy-aware mechanism design. In: 13th conference on electronic commerce. ACM, New York, pp 774–789Google Scholar
  13. 13.
    Nissim K, Smorodinsky R, Tennenholtz M (2012) Approximately optimal mechanism design via differential privacy. In: 3rd conference on innovations in theoretical computer science. ACM, New York, pp 203–213Google Scholar
  14. 14.
    Pai MM, Roth A (2013) Privacy and mechanism design. ACM SIGecom Exch 12(1):8–29CrossRefGoogle Scholar
  15. 15.
    Vickrey W (1961) Counterspeculation, auctions, and competitive sealed tenders. J Financ 16(1):8–37MathSciNetCrossRefGoogle Scholar
  16. 16.
    Vondrák J (2008) Optimal approximation for the submodular welfare problem in the value oracle model. In: 40th annual symposium on theory of computing. ACM, New York, pp 67–74Google Scholar
  17. 17.
    Xiao D (2013) Is privacy compatible with truthfulness? In: 4th conference on innovations in theoretical computer science. ACM, New York, pp 67–86Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer Science, The University of Hong KongHong KongHong kong