Published July 11, 2022
| Version Published + Submitted
Book Section - Chapter
Open
Private Private Information
Creators
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1.
University of Pennsylvania
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2.
California Institute of Technology
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.
Additional Information
© 2022 Copyright held by the owner/author(s). Fedor Sandomirskiy was supported by the Linde Institute at Caltech and the National Science Foundation (grant CNS 1518941). Omer Tamuz was supported by a grant from the Simons Foundation (#419427), a Sloan fellowship, a BSF award (#2018397) and a National Science Foundation CAREER award (DMS-1944153).Attached Files
Published - 3490486.3538348.pdf
Submitted - 2112.14356.pdf
Files
2112.14356.pdf
Additional details
Identifiers
- Eprint ID
- 115372
- Resolver ID
- CaltechAUTHORS:20220707-170554297
Related works
- Describes
- https://arxiv.org/abs/2112.14356 (URL)
Funding
- Linde Institute of Economic and Management Science
- NSF
- CNS-1518941
- Simons Foundation
- 419427
- Alfred P. Sloan Foundation
- Binational Science Foundation (USA-Israel)
- 2018397
- NSF
- DMS-1944153
Dates
- Created
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2022-07-07Created from EPrint's datestamp field
- Updated
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2022-07-27Created from EPrint's last_modified field
Caltech Custom Metadata
- Caltech groups
- Mathematics Department