CaltechAUTHORS
  A Caltech Library Service

Private Private Information

He, Kevin and Sandomirskiy, Fedor and Tamuz, Omer (2022) Private Private Information. In: Proceedings of the 23rd ACM Conference on Economics and Computation. Association for Computing Machinery , New York, NY, p. 1145. ISBN 978-1-4503-9150-4. https://resolver.caltech.edu/CaltechAUTHORS:20220707-170554297

[img] PDF - Published Version
See Usage Policy.

770kB
[img] PDF - Submitted Version
See Usage Policy.

546kB

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20220707-170554297

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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1145/3490486.3538348DOIArticle
https://arxiv.org/abs/2112.14356arXivDiscussion Paper
ORCID:
AuthorORCID
He, Kevin0000-0001-5806-0370
Sandomirskiy, Fedor0000-0001-9886-3688
Tamuz, Omer0000-0002-0111-0418
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).
Funders:
Funding AgencyGrant Number
Linde Institute of Economic and Management ScienceUNSPECIFIED
NSFCNS-1518941
Simons Foundation419427
Alfred P. Sloan FoundationUNSPECIFIED
Binational Science Foundation (USA-Israel)2018397
NSFDMS-1944153
Subject Keywords:game theory, information design, beliefs
DOI:10.1145/3490486.3538348
Record Number:CaltechAUTHORS:20220707-170554297
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220707-170554297
Official Citation:Kevin He, Fedor Sandomirskiy, and Omer Tamuz. 2022. Private Private Information. In Proceedings of the 23rd ACM Conference on Economics and Computation (EC ’22), July 11–15, 2022, Boulder, CO, USA. ACM, New York, NY, USA, 1 page. https://doi.org/10.1145/3490486.3538348
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115372
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:07 Jul 2022 19:44
Last Modified:27 Jul 2022 19:20

Repository Staff Only: item control page