  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.
