skip to main content
research-article

Truthful Mechanisms for Agents That Value Privacy

Published:18 March 2016Publication History
Skip Abstract Section

Abstract

Recent work has constructed economic mechanisms that are both truthful and differentially private. In these mechanisms, privacy is treated separately from truthfulness; it is not incorporated in players’ utility functions (and doing so has been shown to lead to nontruthfulness in some cases). In this work, we propose a new, general way of modeling privacy in players’ utility functions. Specifically, we only assume that if an outcome o has the property that any report of player i would have led to o with approximately the same probability, then o has a small privacy cost to player i. We give three mechanisms that are truthful with respect to our modeling of privacy: for an election between two candidates, for a discrete version of the facility location problem, and for a general social choice problem with discrete utilities (via a VCG-like mechanism). As the number n of players increases, the social welfare achieved by our mechanisms approaches optimal (as a fraction of n).

References

  1. Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim. 2005. Practical privacy: The SuLQ framework. In PODS, Chen Li (Ed.). ACM, 128--138. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Felix Brandt and Tuomas Sandholm. 2008. On the existence of unconditionally privacy-preserving auction protocols. ACM Trans. Inf. Syst. Secur. 11, 2, Article 6 (May 2008), 21 pages. DOI:http://dx.doi.org/10.1145/1330332.1330338 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Thomas M. Cover and Joy A. Thomas. 1991. Elements of Information Theory (2nd ed.). John Wiley & Sons. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Irit Dinur and Kobbi Nissim. 2003. Revealing information while preserving privacy. In PODS. ACM, 202--210. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Yevgeniy Dodis, Shai Halevi, and Tal Rabin. 2000. A cryptographic solution to a game theoretic problem. In CRYPTO (Lecture Notes in Computer Science), Mihir Bellare (Ed.), Vol. 1880. Springer, 112--130. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. Calibrating noise to sensitivity in private data analysis. In TCC (Lecture Notes in Computer Science), Shai Halevi and Tal Rabin (Eds.), Vol. 3876. Springer, 265--284. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Cynthia Dwork and Kobbi Nissim. 2004. Privacy-preserving datamining on vertically partitioned databases. In CRYPTO (Lecture Notes in Computer Science), Matthew K. Franklin (Ed.), Vol. 3152. Springer, 528--544.Google ScholarGoogle Scholar
  8. Cynthia Dwork and Aaron Roth. 2014. The Algorithmic Foundations of Differential Privacy. Now Publishers. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Cynthia Dwork, Guy N. Rothblum, and Salil P. Vadhan. 2010. Boosting and differential privacy. In FOCS. IEEE Computer Society, 51--60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Joan Feigenbaum, Aaron D. Jaggard, and Michael Schapira. 2010. Approximate privacy: Foundations and quantification (extended abstract). In ACM Conference on Electronic Commerce, David C. Parkes, Chrysanthos Dellarocas, and Moshe Tennenholtz (Eds.). ACM, 167--178. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Arpita Ghosh and Aaron Roth. 2011. Selling privacy at auction. In Proceedings of the 12th ACM Conference on Electronic Commerce (EC’11). ACM, New York, NY, 199--208. DOI:http://dx.doi.org/10.1145/1993574.1993605 Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Ronen Gradwohl. 2012. Privacy in Implementation. Working Paper. (November 2012).Google ScholarGoogle Scholar
  13. Ronen Gradwohl and Rann Smorodinsky. 2014. Subjective Perception Games and Privacy. arXiv:1409l.1487. (September 2014).Google ScholarGoogle Scholar
  14. Zhiyi Huang and Sampath Kannan. 2012. The exponential mechanism for social welfare: Private, truthful, and nearly optimal. In FOCS. IEEE Computer Society, 140--149. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Sergei Izmalkov, Silvio Micali, and Matt Lepinski. 2005. Rational secure computation and ideal mechanism design. In FOCS. IEEE Computer Society, 585--595. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Shiva Prasad Kasiviswanathan and Adam Smith. 2008. A note on differential privacy: Defining resistance to arbitrary side information. CoRR abs/0803.3946 (2008).Google ScholarGoogle Scholar
  17. Frank McSherry and Kunal Talwar. 2007. Mechanism design via differential privacy. In FOCS. IEEE Computer Society, 94--103. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Moni Naor, Benny Pinkas, and Reuban Sumner. 1999. Privacy preserving auctions and mechanism design. In ACM Conference on Electronic Commerce. 129--139. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Kobbi Nissim, Claudio Orlandi, and Rann Smorodinsky. 2012a. Privacy-aware mechanism design. In ACM Conference on Electronic Commerce (EC’12). 774--789. DOI:http://dx.doi.org/10.1145/2229012.2229073 Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Kobbi Nissim, Rann Smorodinsky, and Moshe Tennenholtz. 2012b. Approximately optimal mechanism design via differential privacy. In Innovations in Theoretical Computer Science 2012. 203--213. DOI:http://dx.doi.org/10.1145/2090236.2090254 Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Mallesh M. Pai and Aaron Roth. 2013. Privacy and mechanism design. ACM SIGecom Exchanges 12 (June 2013), 8--29. Issue 1. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. David C. Parkes, Michael O. Rabin, Stuart M. Shieber, and Christopher Thorpe. 2008. Practical secrecy-preserving, verifiably correct and trustworthy auctions. Electronic Commerce Res. Appl. 7, 3 (2008), 294--312. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. David Xiao. 2011. Is privacy compatible with truthfulness? Cryptology ePrint Archive, Report 2011/005. (2011).Google ScholarGoogle Scholar
  24. David Xiao. 2013. Is privacy compatible with truthfulness? In ITCS, Robert D. Kleinberg (Ed.). ACM, 67--86. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Truthful Mechanisms for Agents That Value Privacy

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image ACM Transactions on Economics and Computation
        ACM Transactions on Economics and Computation  Volume 4, Issue 3
        Special Issue on EC'13
        June 2016
        162 pages
        ISSN:2167-8375
        EISSN:2167-8383
        DOI:10.1145/2905047
        Issue’s Table of Contents

        Copyright © 2016 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 18 March 2016
        • Accepted: 1 October 2015
        • Received: 1 February 2015
        Published in teac Volume 4, Issue 3

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader