An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices
August 2018
Working Paper Number:
CES-18-35
Abstract
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 U.S. 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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economist,
statistical,
disclosure,
agency,
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expenditure,
privacy,
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discrimination,
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Internal Revenue Service,
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National Science Foundation,
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Office of Management and Budget,
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Department of Economics,
Federal Reserve System,
Department of Agriculture,
Department of Justice,
Cornell University,
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Economic Census,
Social Security Number,
Alfred P Sloan Foundation,
Sloan Foundation,
National Center for Health Statistics,
Public Use Micro Sample,
National Institutes of Health,
National Academy of Sciences,
Quarterly Census of Employment and Wages,
2010 Census
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