IDEAS home Printed from https://ideas.repec.org/p/cen/wpaper/19-13.html
   My bibliography  Save this paper

Releasing Earnings Distributions using Differential Privacy: Disclosure Avoidance System For Post Secondary Employment Outcomes (PSEO)

Author

Listed:
  • Andrew Foote
  • Ashwin Machanavajjhala
  • Kevin McKinney

Abstract

The U.S. Census Bureau recently released data on earnings percentiles of graduates from post secondary institutions. This paper describes and evaluates the disclosure avoidance system developed for these statistics. We propose a differentially private algorithm for releasing these data based on standard differentially private building blocks, by constructing a histogram of earnings and the application of the Laplace mechanism to recover a differentially-private CDF of earnings. We demonstrate that our algorithm can release earnings distributions with low error, and our algorithm out-performs prior work based on the concept of smooth sensitivity from Nissim, Raskhodnikova and Smith (2007).

Suggested Citation

  • Andrew Foote & Ashwin Machanavajjhala & Kevin McKinney, 2019. "Releasing Earnings Distributions using Differential Privacy: Disclosure Avoidance System For Post Secondary Employment Outcomes (PSEO)," Working Papers 19-13, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:19-13
    as

    Download full text from publisher

    File URL: https://www2.census.gov/ces/wp/2019/CES-WP-19-13.pdf
    File Function: First version, 2019
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kevin L. McKinney & John M. Abowd & John Sabelhaus, 2021. "United States Earnings Dynamics: Inequality, Mobility, and Volatility," NBER Chapters, in: Measuring Distribution and Mobility of Income and Wealth, pages 69-104, National Bureau of Economic Research, Inc.
    2. 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.
    3. Katharine G. Abraham & Ron S. Jarmin & Brian C. Moyer & Matthew D. Shapiro, 2020. "Introduction: Big Data for Twenty-First-Century Economic Statistics: The Future Is Now," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 1-22, National Bureau of Economic Research, Inc.
    4. Andrew Foote, 2022. "Measuring Protection-Induced Errors in Earnings Outcomes from PSEO," CES Technical Notes Series 22-01, Center for Economic Studies, U.S. Census Bureau.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cen:wpaper:19-13. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dawn Anderson (email available below). General contact details of provider: https://edirc.repec.org/data/cesgvus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.