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.

翻译：我们研究了在个体报告偏好经过噪声隐私保护转换后，公共品供给的机制设计问题。该设定是包含转移支付和拟线性效用的标准二元模型。代理人报告其对公共品的偏好，这些偏好会被随机"翻转"，使得任何个体报告都可以被解释为噪声结果。我们研究了在存在噪声的情况下保持公共决策（噪声灵敏度）、追求效率以及减轻噪声对收入影响之间的权衡关系。