An Economic Analysis of User-Privacy Options in Ad-Supported Services

  • Joan Feigenbaum
  • Michael Mitzenmacher
  • Georgios Zervas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7695)


We analyze the value to e-commerce website operators of offering privacy options to users, e.g., of allowing users to opt out of ad targeting. In particular, we assume that site operators have some control over the cost that a privacy option imposes on users and ask when it is to their advantage to make such costs low. We consider both the case of a single site and the case of multiple sites that compete both for users who value privacy highly and for users who value it less. One of our main results in the case of a single site is that, under normally distributed utilities, if a privacy-sensitive user is worth at least \(\sqrt{2} - 1\) times as much to advertisers as a privacy-insensitive user, the site operator should strive to make the cost of a privacy option as low as possible. In the case of multiple sites, we show how a Prisoner’s-Dilemma situation can arise: In the equilibrium in which both sites are obliged to offer a privacy option at minimal cost, both sites obtain lower revenue than they would if they colluded and neither offered a privacy option.


Private Service Private User Search Service Online Advertising Privacy Enforcement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Acquisti, A., Varian, H.: Conditioning prices on purchase history. Marketing Science 24(3), 367–381 (2005)CrossRefGoogle Scholar
  2. 2.
    Beales, H.: The value of behavioral targeting,
  3. 3.
    Carrascal, J.P., Riederer, C., Erramilli, V., Cherubini, M., de Oliveira, R.: Your browsing behavior for a big mac: Economics of personal information online,
  4. 4.
    Chen, Y., Pavlov, D., Canny, J.F.: Large-scale behavioral targeting. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), pp. 209–218 (2009)Google Scholar
  5. 5.
    Conitzer, V., Taylor, C., Wagman, L.: Hide and seek: Costly consumer privacy in a market with repeat purchases. Marketing Science 31(2), 277–292 (2012)CrossRefGoogle Scholar
  6. 6.
    Goldfarb, A., Tucker, C.E.: Online advertising, behavioral targeting, and privacy. Communications of the ACM 54(5), 25–27 (2011)CrossRefGoogle Scholar
  7. 7.
    Ito, K., McKean, H.: Diffusion processes and their sample paths. Springer, Heidelberg (1965)zbMATHCrossRefGoogle Scholar
  8. 8.
    Iyer, G., Soberman, D., Villas-Boas, M.: The targeting of advertising. Marketing Science 24(3), 461–476 (2005)CrossRefGoogle Scholar
  9. 9.
    Jansen, B.J., Solomon, L.: Gender demographic targeting in sponsored search. In: Proceedings of the 28th ACM International Conference on Human Factors in Computing Systems (CHI 2010), pp. 831–840 (2010)Google Scholar
  10. 10.
    Riederer, C., Erramilli, V., Chaintreau, A., Krishnamurthy, B., Rodriguez, P.: For sale: Your data, by: You. In: Proceedings of the 10th ACM Workshop on Hot Topics in Networks, HotNets-X (2011)Google Scholar
  11. 11.
    Ruskai, M.B., Werner, E.: A pair of optimal inequalities related to the error function,
  12. 12.
    Telang, R., Rahan, U., Mukhopadhyay, T.: The market structure for internet search engines. Journal of MIS 21(2), 137–160 (2004)Google Scholar
  13. 13.
    Yan, J., Liu, N., Wang, G., Zhang, W., Jiang, Y., Chen, Z.: How much can behavioral targeting help online advertising? In: Proceedings of the 18th International World Wide Web Conference (WWW 2009), pp. 261–270 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Joan Feigenbaum
    • 1
  • Michael Mitzenmacher
    • 2
  • Georgios Zervas
    • 1
  1. 1.Computer Science DepartmentYale UniversityUSA
  2. 2.School of Engineering & Applied SciencesHarvard UniversityUSA

Personalised recommendations