Binary mechanisms under privacy-preserving noise
Creators
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1.
California Institute of Technology
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2.
University of California, Berkeley
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.
Copyright and License
© 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Contributions
Farzad Pourbabaee: Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization. Federico Echenique: Writing – review & editing, Writing – original draft, Methodology, Investigation, Conceptualization.
Conflict of Interest
Regarding the paper “Binary Mechanisms under Privacy-Preserving Noise,” submitted for publication in the Journal of Economic Theory, we declare that we have relevant financial interests to declare.
Data Availability
No data was used for the research described in the article.
Additional details
Related works
- Is new version of
- Discussion Paper: arXiv:2301.06967 (arXiv)
Dates
- Accepted
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2025-01-06Accepted
- Available
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2025-01-10Available online
- Available
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2025-01-15Version of Record
Caltech Custom Metadata
- Publication Status
- Published