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Privacy games. (English) Zbl 1404.91001
Liu, Tie-Yan (ed.) et al., Web and internet economics. 10th international conference, WINE 2014, Beijing, China, December 14–17, 2014. Proceedings. Cham: Springer (ISBN 978-3-319-13128-3/pbk). Lecture Notes in Computer Science 8877, 371-385 (2014).
Summary: The problem of analyzing the effect of privacy concerns on the behavior of selfish utility-maximizing agents has received much attention lately. Privacy concerns are often modeled by altering the utility functions of agents to consider also their privacy loss [D. Xiao, in: Proceedings of the 4th conference on innovations in theoretical computer science, ITCS’13. New York, NY: Association for Computing Machinery (ACM). 67–86 (2013; Zbl 1361.68076); A. Ghosh and A. Roth, Games Econ. Behav. 91, 334–346 (2015; Zbl 1318.91093); K. Nissim et al., “Privacy-aware mechanism design”, in: Proceedings of the 13th ACM conference on electronic commerce, EC’12. New York, NY: Association for Computing Machinery (ACM). 774–789 (2012; doi:10.1145/2229012.2229073); Y. Chen et al., “Truthful mechanisms for agents that value privacy”, in: Proceedings of the 14th ACM conference on electronic commerce, EC’13. New York, NY: Association for Computing Machinery (ACM). 215–232 (2013; doi:10.1145/2492002.2482549)]. Such privacy aware agents prefer to take a randomized strategy even in very simple games in which non-privacy aware agents play pure strategies. In some cases, the behavior of privacy aware agents follows the framework of randomized response, a well-known mechanism that preserves differential privacy.
Our work is aimed at better understanding the behavior of agents in settings where their privacy concerns are explicitly given. We consider a toy setting where agent \(A\), in an attempt to discover the secret type of agent \(B\), offers \(B\) a gift that one type of \(B\) agent likes and the other type dislikes. As opposed to previous works, \(B\)’s incentive to keep her type a secret isn’t the result of “hardwiring” \(B\)’s utility function to consider privacy, but rather takes the form of a payment between \(B\) and \(A\). We investigate three different types of payment functions and analyze \(B\)’s behavior in each of the resulting games. As we show, under some payments, \(B\)’s behavior is very different than the behavior of agents with hardwired privacy concerns and might even be deterministic. Under a different payment we show that \(B\)’s BNE strategy does fall into the framework of randomized response.
For the entire collection see [Zbl 1302.68013].

MSC:
91A05 2-person games
91B26 Auctions, bargaining, bidding and selling, and other market models
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