Encyclopedia of Wireless Networks

Living Edition
| Editors: Xuemin (Sherman) Shen, Xiaodong Lin, Kuan Zhang

Privacy Game

  • Hang ShenEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32903-1_312-1



In general, privacy game can be defined as individual and/or cooperative behaviors between the user and the adversary (attacker) that choose a certain defense/attack strategy/action to maximize their self-interest.

Take Stackelberg privacy game (Shokri 2015; Shokri et al. 2012) between the user and the adversary as an example. The user plays first by choosing a privacy protection mechanism and committing to it by running it on his or her actual data. The follower (adversary) plays next by inferring the user’s data, knowing the protection mechanism that the user has committed to. Specifically, the Stackelberg privacy game is defined as:
  1. 1.

    Nature selects a data (i.e., user secret that contains privacy information) for the user, where the data are selected according to a probability distribution function.

  2. 2.

    Given the chosen data, the user runs a protection mechanism to generate a disguised data...

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  1. Acquisti A, Dingledine R, Syverson P (2003) On the economics of anonymity. In: Proceedings of the Financial Cryptography (FC), Lecture Notes in Computer Science, Springer, vol 2742. pp 84–102Google Scholar
  2. Chaum D (1981) Untraceable electronicmail, return addresses, and digital pseudonyms. Commun ACM 24(2):84–88CrossRefGoogle Scholar
  3. Freudiger J, Manshaei MH, Hubaux J-P, Parkes DC (2009) On non-cooperative location privacy: a game-theoretic analysis. In: Proceedings of the ACM conference on computer and communications security (CCS), ACM, pp 324–337Google Scholar
  4. Liang X, Xiao Y (2013) Game theory for network security. IEEE Commun Surv Tutorials 15(1):472–486CrossRefGoogle Scholar
  5. Manshaei MH, Zhu Q, Alpcan T, Bacşar T, Hubaux JP (2013) Game theory meets network security and privacy. ACM Comput Surv 45(3):25CrossRefGoogle Scholar
  6. Nisan N (ed) (2007) Introduction to mechanism design (for computer scientists). In: Proceedings of the algorithmic game theory. Cambridge University Press, Cambridge, UK, pp 209–242Google Scholar
  7. Nisan N, Ronen A (1999) Algorithmic mechanism design. In: Proceedings of the 31 annual ACM symposium on theory of computing, ACM, pp 129–140Google Scholar
  8. Raya M, Shokri R, Hubaux J-P (2010) On the tradeoff between trust and privacy in wireless ad hoc networks. In: Proceedings of the ACM conference on wireless network security (WiSec), ACM, pp 75–80Google Scholar
  9. Shokri R (2015) Privacy games: optimal user-centric data obfuscation. Proc Privacy Enhanc Technol 2015(2):299–315CrossRefGoogle Scholar
  10. Shokri R, Theodorakopoulos G, Troncoso C, Hubaux JP, Le Boudec JY (2012) Protecting location privacy: optimal strategy against localization attacks. In: Proceedings of the ACM conference on computer and communications security (CCS), ACM, pp 617–627Google Scholar
  11. Zhang N, Yu W, Fu X, Das SK (2010) gPath: a game-theoretic path selection algorithm to protect Tor’s anonymity. In: Proceedings of the 1st conference on decision and game theory for security (GameSec), Lecture Notes in Computer Science, Springer, vol 6442. pp 58–72Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and TechnologyNanjing Tech UniversityNanjingChina

Section editors and affiliations

  • Haojin Zhu
    • 1
  • Jian Shen
    • 2
  1. 1.Shanghai Jiaotong University, ChinaShanghaiChina
  2. 2.Nanjing University of Information Science & Technology, ChinaNanjingChina