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Privacy Can Arise Endogenously in an Economic System with Learning Agents

Authors Nivasini Ananthakrishnan, Tiffany Ding, Mariel Werner, Sai Praneeth Karimireddy, Michael I. Jordan

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Author Details

Nivasini Ananthakrishnan
  • University of California, Berkeley, CA, USA
Tiffany Ding
  • University of California, Berkeley, CA, USA
Mariel Werner
  • University of California, Berkeley, CA, USA
Sai Praneeth Karimireddy
  • University of California, Berkeley, CA, USA
Michael I. Jordan
  • University of California, Berkeley, CA, USA

Funding

TD acknowledges support from the National Science Foundation Graduate Research Fellowship Program under grant no. 2146752, SPK is partially supported by a Mobility Fellowship by the Swiss National Science Foundation. We also acknowledge support from the European Union (ERC-2022-SYG-OCEAN-101071601).

Acknowledgements

We thank Alireza Fallah and Stephen Bates for helpful discussions.

Cite AsGet BibTex

Nivasini Ananthakrishnan, Tiffany Ding, Mariel Werner, Sai Praneeth Karimireddy, and Michael I. Jordan. Privacy Can Arise Endogenously in an Economic System with Learning Agents. In 5th Symposium on Foundations of Responsible Computing (FORC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 295, pp. 9:1-9:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.FORC.2024.9

Abstract

We study price-discrimination games between buyers and a seller where privacy arises endogenously - that is, utility maximization yields equilibrium strategies where privacy occurs naturally. In this game, buyers with a high valuation for a good have an incentive to keep their valuation private, lest the seller charge them a higher price. This yields an equilibrium where some buyers will send a signal that misrepresents their type with some probability; we refer to this as buyer-induced privacy. When the seller is able to publicly commit to providing a certain privacy level, we find that their equilibrium response is to commit to ignore buyers' signals with some positive probability; we refer to this as seller-induced privacy. We then turn our attention to a repeated interaction setting where the game parameters are unknown and the seller cannot credibly commit to a level of seller-induced privacy. In this setting, players must learn strategies based on information revealed in past rounds. We find that, even without commitment ability, seller-induced privacy arises as a result of reputation building. We characterize the resulting seller-induced privacy and seller’s utility under no-regret and no-policy-regret learning algorithms and verify these results through simulations.

Subject Classification

ACM Subject Classification
  • Theory of computation → Theory and algorithms for application domains
Keywords
  • Privacy
  • Game Theory
  • Online Learning
  • Price Discrimination

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References

  1. Alessandro Acquisti, Curtis Taylor, and Liad Wagman. The economics of privacy. Journal of Economic Literature, 54(2):442-492, 2016. Google Scholar
  2. Alessandro Acquisti and Hal Varian. Conditioning prices on purchase history. Marketing Science, 2004. Google Scholar
  3. Raman Arora, Michael Dinitz, Teodor V. Marinov, and Mehryar Mohri. Policy regret in repeated games. Advances in Neural Information Processing Systems, 2020. Google Scholar
  4. Modibo K. Camara, Jason D. Hartline, and Aleck Johnsen. Mechanisms for a no-regret agent: Beyond the common prior. 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS), 2020. Google Scholar
  5. Vincent Conitzer, Curtis Taylor, and Liad Wagman. Hide and seek: Costly consumer privacy in a market with repeated purchases. Marketing Science, 31(2):277-292, 2012. Google Scholar
  6. Yuan Deng, Jon Schneider, and Balasubramanian Sivan. Strategizing against no-regret learners. Advances in Neural Information Processing Systems, 32, 2019. Google Scholar
  7. Jinshuo Dong, Aaron Roth, and Weijie J Su. Gaussian differential privacy. arXiv preprint arXiv:1905.02383, 2019. Google Scholar
  8. Cynthia Dwork and Aaron Roth. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3-4):211-407, 2014. Google Scholar
  9. Jeffrey Ely and Juuso Valimaki. Bad reputation. The Quarterly Journal of Economics, 118(3):785-814, 2003. Google Scholar
  10. Dean P Foster and Rakesh V Vohra. Calibrated learning and correlated equilibrium. Games and Economic Behavior, 21(1-2):40, 1997. Google Scholar
  11. John Bellamy Foster and Robert McChesney. Surveillance capitalism. Monthly review, 66(3):1-31, 2014. Google Scholar
  12. Drew Fudenberg and J Miguel Villas-Boas. Behavior-based price discrimination and customer recognition. Handbook on Economics and Information Systems, 1:377-436, 2006. Google Scholar
  13. Nika Haghtalab, Chara Podimata, and Kunhe Yang. Calibrated Stackelberg games: Learning optimal commitments against calibrated agents. Advances in Neural Information Processing Systems, 36, 2024. Google Scholar
  14. Oliver D Hart and Jean Tirole. Contract renegotiation and coasian dynamics. The Review of Economic Studies, 55(4):509-540, 1988. Google Scholar
  15. Johannes Horner. Reputation and competition. American Economic Review, 92(3):644-663, 2002. Google Scholar
  16. Shota Ichihashi. Online privacy and information disclosure by consumers. American Economic Review, 110(2):569-595, 2020. Google Scholar
  17. Tor Lattimore and Csaba Szepesvari. Bandit Algorithms. Cambridge University Press, 2020. Google Scholar
  18. Katrina Ligett and Kobbi Nissim. We need to focus on how our data is used, not just how it is shared. Communications of the ACM, 66(9):32-34, 2023. Google Scholar
  19. Ilya Mironov. Rényi differential privacy. In 2017 IEEE 30th Computer Security Foundations Symposium (CSF), pages 263-275. IEEE, 2017. Google Scholar
  20. Rodrigo Montes, Wilfried Sand-Zantman, and Tommaso Valletti. The value of personal information in markets with endogenous privacy. Center for Economic and International Studies, 13(352), 2015. Google Scholar
  21. Kobbi Nissim, Aaron Bembenek, Alexandra Wood, Mark Bun, Marco Gaboardi, Urs Gasser, David R O'Brien, Thomas Steinke, and Salil Vadhan. Bridging the gap between computer science and legal approaches to privacy. Harvard Journal of Law & Technology, 31:687, 2017. Google Scholar
  22. Carl Shapiro. Premiums for high quality products are returns to reputation. Quarterly Journal of Economics, 98(4):659-679, 1983. Google Scholar
  23. Alicia Solow-Niederman. Information privacy and the inference economy. Northwestern University Law Review, 117:357, 2022. Google Scholar
  24. Jun Tang, Aleksandra Korolova, Xiaolong Bai, Xueqiang Wang, and Xiaofeng Wang. Privacy loss in Apple’s implementation of differential privacy on macOS 10.12. arXiv preprint arXiv:1709.02753, 2017. Google Scholar
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