Abstract
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 [24,13,19,4]. 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.
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Chen, Y., Sheffet, O., Vadhan, S. (2014). Privacy Games. In: Liu, TY., Qi, Q., Ye, Y. (eds) Web and Internet Economics. WINE 2014. Lecture Notes in Computer Science, vol 8877. Springer, Cham. https://doi.org/10.1007/978-3-319-13129-0_30
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DOI: https://doi.org/10.1007/978-3-319-13129-0_30
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