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A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples

Author

Listed:
  • Raj Chetty
  • John N. Friedman

Abstract

Building on insights from the differential privacy literature, we develop a simple noise-infusion method to reduce privacy loss when disclosing statistics such as OLS regression estimates based on small samples. Although our method does not offer a formal privacy guarantee, it outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias. We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by census tract in the Opportunity Atlas. We provide a step-by-step guide and code to implement our approach.

Suggested Citation

  • Raj Chetty & John N. Friedman, 2019. "A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 414-420, May.
    • Handle: RePEc:aea:apandp:v:109:y:2019:p:414-20
    Note: DOI: 10.1257/pandp.20191109
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    File URL: https://www.aeaweb.org/doi/10.1257/pandp.20191109
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    Cited by:

    1. Atheendar S Venkataramani & Rourke O’Brien & Gregory L Whitehorn & Alexander C Tsai, 2020. "Economic influences on population health in the United States: Toward policymaking driven by data and evidence," PLOS Medicine, Public Library of Science, vol. 17(9), pages 1-17, September.
    2. 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).
    3. Vilhuber, Lars, 2023. "Reproducibility and transparency versus privacy and confidentiality: Reflections from a data editor," Journal of Econometrics, Elsevier, vol. 235(2), pages 2285-2294.
    4. Ian M. Schmutte & Nathan Yoder, 2022. "Information Design for Differential Privacy," Papers 2202.05452, arXiv.org, revised Jul 2024.
    5. Ron S. Jarmin & John M. Abowd & Robert Ashmead & Ryan Cumings-Menon & Nathan Goldschlag & Michael B. Hawes & Sallie Ann Keller & Daniel Kifer & Philip Leclerc & Jerome P. Reiter & Rolando A. Rodrígue, 2023. "An in-depth examination of requirements for disclosure risk assessment," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(43), pages 2220558120-, October.
    6. John Voorheis & Jonathan Colmer & Kendall Houghton & Eva Lyubich & Mary Munro & Cameron Scalera & Jennifer Withrow, 2024. "The Privacy-Protected Gridded Environmental Impacts Frame," Working Papers 24-74, Center for Economic Studies, U.S. Census Bureau.
    7. Lutz Sager & Gregor Singer, 2025. "Clean Identification? The Effects of the Clean Air Act on Air Pollution, Exposure Disparities, and House Prices," American Economic Journal: Economic Policy, American Economic Association, vol. 17(1), pages 1-36, February.
    8. Braathen, Christian & Thorsen, Inge & Ubøe, Jan, 2022. "Adjusting for Cell Suppression in Commuting Trip Data," Discussion Papers 2022/13, Norwegian School of Economics, Department of Business and Management Science.
    9. Dionissi Aliprantis & Hal Martin, 2020. "Neighborhood Sorting Obscures Neighborhood Effects in the Opportunity Atlas," Working Papers 20-37, Federal Reserve Bank of Cleveland.
    10. Craig Wesley Carpenter & Anders Van Sandt & Scott Loveridge, 2022. "Measurement error in US regional economic data," Journal of Regional Science, Wiley Blackwell, vol. 62(1), pages 57-80, January.

    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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