Binary Mechanisms under Privacy-Preserving Noise

TL;DR

This paper explores how to design mechanisms for public-good provision that balance privacy, noise robustness, efficiency, and revenue in a setting where individual preferences are randomly flipped for privacy.

Contribution

It introduces a framework for mechanism design under privacy-preserving noise, analyzing tradeoffs between noise sensitivity, efficiency, and revenue.

Findings

Tradeoffs between privacy, noise robustness, and efficiency are characterized.

Mechanisms can be designed to mitigate noise impact while maintaining revenue.

The impact of noisy reports on public-good provision decisions is quantified.

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.