期刊
AMERICAN ECONOMIC REVIEW
卷 109, 期 1, 页码 171-202出版社
AMER ECONOMIC ASSOC
DOI: 10.1257/aer.20170627
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资金
- Alfred P. Sloan Foundation [G-2015-13903]
- NSF [SES-1131848, BCS-0941226, TC-1012593]
- Center for Labor Economics at UC-Berkeley (EPSRC grant) [EP/K032208/1]
- Isaac Newton Institute for Mathematical Sciences, Cambridge (EPSRC grant) [EP/K032208/1]
- EPSRC [EP/K032208/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/K032208/1] Funding Source: researchfish
- Divn Of Social and Economic Sciences [1131848] Funding Source: National Science Foundation
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.
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