Binary Mechanisms under Privacy-Preserving Noise

Fuente: arXiv
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Main Authors: Pourbabaee, Farzad, Echenique, Federico
Format: Preprint
Published: 2023
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author Pourbabaee, Farzad
Echenique, Federico
author_facet Pourbabaee, Farzad
Echenique, Federico
contents We study mechanism design for public-good provision under a noisy privacy-preserving transformation of individual agents' reported preferences. The setting is a standard binary model with transfers and quasi-linear utility. Agents report their preferences for the public good, which are randomly ``flipped,'' so that any individual report may be explained away as the outcome of noise. We study the tradeoffs between preserving the public decisions made in the presence of noise (noise sensitivity), pursuing efficiency, and mitigating the effect of noise on revenue.
format Preprint
id arxiv_https___arxiv_org_abs_2301_06967
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Binary Mechanisms under Privacy-Preserving Noise
Pourbabaee, Farzad
Echenique, Federico
Theoretical Economics
Computer Science and Game Theory
We study mechanism design for public-good provision under a noisy privacy-preserving transformation of individual agents' reported preferences. The setting is a standard binary model with transfers and quasi-linear utility. Agents report their preferences for the public good, which are randomly ``flipped,'' so that any individual report may be explained away as the outcome of noise. We study the tradeoffs between preserving the public decisions made in the presence of noise (noise sensitivity), pursuing efficiency, and mitigating the effect of noise on revenue.
title Binary Mechanisms under Privacy-Preserving Noise
topic Theoretical Economics
Computer Science and Game Theory
url https://arxiv.org/abs/2301.06967