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

By John M. Abowd, Ian M. Schmutte. 2018

140 citations recorded by the source corpus.

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. (JEL C38, C81, D83)

Subfields: Artificial Intelligence, Economics and Econometrics, Sociology and Political Science, Statistics and Probability, Privacy & Security

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Somesh Jha

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In Privacy & Security

Citations in the corpus

Fair decision making using privacy-protected data (2019, background): “For simplicity, we treat the average per student expenditures exp(Ia ) as public, following [6].”
Automatic Discovery of Privacy–Utility Pareto Fronts (2019, methodology): “Census Bureau to calibrate the level of DP that will be used when releasing the results of the upcoming 2020 census [2, 3, 19].”
Cryptϵ: Crypto-Assisted Differential Privacy on Untrusted Servers (2019, background): “The choice of value for εB should be guided by social prerogatives [7, 61, 69] and is currently outside the scope of Cryptε .”