Binary Mechanisms under Privacy-Preserving Noise

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 mechanisms that seek to preserve the public decisions made in the presence of noise (noise sensitivity), pursue efficiency, and mitigate the effect of noise on revenue. The paper analyzes the trade-offs between these competing considerations.