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Private Private Information

Author

Listed:
  • Kevin He

    (University of Pennsylvania)

  • Fedor Sandomirskiy

    (Caltech)

  • Omer Tamuz

    (Caltech)

Abstract

In a private private information structure, agents’ signals contain no information about the signals of their peers. We study how informative such structures can be, and characterize those that are on the Pareto frontier, in the sense that it is impossible to give more information to any agent without violating privacy. In our main application, we show how to optimally disclose information about an unknown state under the constraint of not revealing anything about a correlated variable that contains sensitive information.

Suggested Citation

  • Kevin He & Fedor Sandomirskiy & Omer Tamuz, 2022. "Private Private Information," PIER Working Paper Archive 22-004, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    • Handle: RePEc:pen:papers:22-004
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    References listed on IDEAS

    as
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