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Differential Privacy and Census Data: Implications for Social and Economic Research

Author

Listed:
  • Steven Ruggles
  • Catherine Fitch
  • Diana Magnuson
  • Jonathan Schroeder

Abstract

The Census Bureau has announced new methods for disclosure control in public use data products. The new approach, known as differential privacy, represents a radical departure from current practice. In its pure form, differential privacy techniques may make the release of useful microdata impossible and limit the utility of tabular small-area data. Adoption of differential privacy will have far-reaching consequences for research. It is likely that scientists, planners, and the public will lose the free access we have enjoyed for six decades to reliable public Census Bureau data describing US social and economic change.

Suggested Citation

  • Steven Ruggles & Catherine Fitch & Diana Magnuson & Jonathan Schroeder, 2019. "Differential Privacy and Census Data: Implications for Social and Economic Research," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 403-408, May.
    • Handle: RePEc:aea:apandp:v:109:y:2019:p:403-08
    Note: DOI: 10.1257/pandp.20191107
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    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20191107
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    Citations

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    Cited by:

    1. Michler, Jeffrey D. & Josephson, Anna & Kilic, Talip & Murray, Siobhan, 2022. "Privacy protection, measurement error, and the integration of remote sensing and socioeconomic survey data," Journal of Development Economics, Elsevier, vol. 158(C).
    2. John M. Abowd & Ian M. Schmutte & William Sexton & Lars Vilhuber, 2019. "Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods," Papers 1906.09353, arXiv.org.
    3. Christine P. Chai, 2022. "Christine P. Chai's contribution to the Discussion of ‘Gaussian Differential Privacy’ by Dong et al," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(1), pages 43-44, February.
    4. J. Tom Mueller & Alexis R. Santos-Lozada, 2022. "The 2020 US Census Differential Privacy Method Introduces Disproportionate Discrepancies for Rural and Non-White Populations," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(4), pages 1417-1430, August.
    5. James Gaboardi, 2020. "Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance," Working Papers 20-05, Center for Economic Studies, U.S. Census Bureau.
    6. Sigurd Dyrting & Abraham Flaxman & Ethan Sharygin, 2022. "Reconstruction of age distributions from differentially private census data," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(6), pages 2311-2329, December.
    7. Hou, Bohan, 2021. "A Novel Data Governance Scheme Based on the Behavioral Economics Theory," SocArXiv 2b9dc, Center for Open Science.
    8. Cinzia Carota & Maurizio Filippone & Silvia Polettini, 2022. "Assessing Bayesian Semi‐Parametric Log‐Linear Models: An Application to Disclosure Risk Estimation," International Statistical Review, International Statistical Institute, vol. 90(1), pages 165-183, April.
    9. Beth Jarosz, 2021. "Poisson Distribution: A Model for Estimating Households by Household Size," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 40(2), pages 149-162, April.
    10. Evan S. Totty & Thor Watson, 2024. "Privacy Protection and Accuracy: What Do We Know? Do We Know Things?? Let's Find Out!," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, National Bureau of Economic Research, Inc.
    11. Arthur Acolin & Ari Decter-Frain & Matt Hall, 2022. "Small-area estimates from consumer trace data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(27), pages 843-882.
    12. James Gaboardi, 2020. "Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance," Working Papers 20-5, Center for Economic Studies, U.S. Census Bureau.

    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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