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Bayesian Mechanism Design with Efficiency, Privacy, and Approximate Truthfulness

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Internet and Network Economics (WINE 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7695))

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Abstract

Recently, there has been a number of papers relating mechanism design and privacy (e.g., see [1-6]). All of these papers consider a worst-case setting where there is no probabilistic information about the players’ types. In this paper, we investigate mechanism design and privacy in the Bayesian setting, where the players’ types are drawn from some common distribution. We adapt the notion of differential privacy to the Bayesian mechanism design setting, obtaining Bayesian differential privacy. We also define a robust notion of approximate truthfulness for Bayesian mechanisms, which we call persistent approximate truthfulness. We give several classes of mechanisms (e.g., social welfare mechanisms and histogram mechanisms) that achieve both Bayesian differential privacy and persistent approximate truthfulness. These classes of mechanisms can achieve optimal (economic) efficiency, and do not use any payments. We also demonstrate that by considering the above mechanisms in a modified mechanism design model, the above mechanisms can achieve actual truthfulness.

Work supported in part by NSF grants IIS-0534064, IIS-0812045, IIS-0911036, CCF-0746990; AFOSR grants FA9550-08-1-0438, FA9550-09-1-0266, FA9550-10-1-0093; ARO grant W911NF-09-1-0281. We thank Joseph Y. Halpern for helpful discussions.

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Leung, S., Lui, E. (2012). Bayesian Mechanism Design with Efficiency, Privacy, and Approximate Truthfulness. In: Goldberg, P.W. (eds) Internet and Network Economics. WINE 2012. Lecture Notes in Computer Science, vol 7695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35311-6_5

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  • DOI: https://doi.org/10.1007/978-3-642-35311-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35310-9

  • Online ISBN: 978-3-642-35311-6

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