Binary Mechanisms under Privacy-Preserving Noise
Theoretical Economics
2024-05-21 v3 Computer Science and Game Theory
Abstract
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.
Cite
@article{arxiv.2301.06967,
title = {Binary Mechanisms under Privacy-Preserving Noise},
author = {Farzad Pourbabaee and Federico Echenique},
journal= {arXiv preprint arXiv:2301.06967},
year = {2024}
}