Private Private Information

Fuente: arXiv
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Bibliographische Detailangaben
Hauptverfasser: He, Kevin, Sandomirskiy, Fedor, Tamuz, Omer
Format: Preprint
Veröffentlicht: 2021
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author He, Kevin
Sandomirskiy, Fedor
Tamuz, Omer
author_facet He, Kevin
Sandomirskiy, Fedor
Tamuz, Omer
contents Private signals model noisy information about an unknown state. Although these signals are called "private," they may still carry information about each other. Our paper introduces the concept of private private signals, which contain information about the state but not about other signals. To achieve privacy, signal quality may need to be sacrificed. We study the informativeness of private private signals and characterize those that are optimal in the sense that they cannot be made more informative without violating privacy. We discuss implications for privacy in recommendation systems, information design, causal inference, and mechanism design.
format Preprint
id arxiv_https___arxiv_org_abs_2112_14356
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Private Private Information
He, Kevin
Sandomirskiy, Fedor
Tamuz, Omer
Theoretical Economics
Computer Science and Game Theory
Probability
Private signals model noisy information about an unknown state. Although these signals are called "private," they may still carry information about each other. Our paper introduces the concept of private private signals, which contain information about the state but not about other signals. To achieve privacy, signal quality may need to be sacrificed. We study the informativeness of private private signals and characterize those that are optimal in the sense that they cannot be made more informative without violating privacy. We discuss implications for privacy in recommendation systems, information design, causal inference, and mechanism design.
title Private Private Information
topic Theoretical Economics
Computer Science and Game Theory
Probability
url https://arxiv.org/abs/2112.14356