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  Original-Sources-and-Research-Concerning-Census-Bureau 10/20/2021 10:46:AM
  Title_I 10/20/2021 10:46:AM
abowd-schmutte-privacy_WP.pdf application/pdf 512.8 KB 10/12/2019 03:16:AM

Project Description

Summary:  View help for Summary 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.

Scope of Project

Subject Terms:  View help for Subject Terms [economics, public goods, algorithmic fairness, data privacy, differential privacy]
JEL Classification:  View help for JEL Classification
      C38 Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models
      C81 Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Geographic Coverage:  View help for Geographic Coverage United States
Data Type(s):  View help for Data Type(s) [survey data, administrative records data]
Collection Notes:  View help for Collection Notes This is an archive of data publicly available elsewhere

Methodology

Unit(s) of Observation:  View help for Unit(s) of Observation school district, state,

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