An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices
将统计机构面临的隐私保护与准确性权衡视为资源分配问题，提出边际成本等于边际收益的经济学解决方案，并利用差分隐私算法建模，为美国统计项目提供决策指导。
Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from US statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy.