An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices

• 成果类型: Article
• 署名作者: Abowd, John M.; Schmutte, Ian M.
• 署名单位: Cornell University; University System of Georgia; University of Georgia
• 刊物名称: AMERICAN ECONOMIC REVIEW
• ISSN/ISSBN: 0002-8282
• DOI: 10.1257/aer.20170627
• 发表日期: 2019
• 页码: 171-202
• 关键词: uncertainty; mechanism

摘要

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.