IDEAS home Printed from https://ideas.repec.org/p/cen/wpaper/14-30.html
   My bibliography  Save this paper

Noise Infusion As A Confidentiality Protection Measure For Graph-Based Statistics

Author

Listed:
  • John M. Abowd
  • Kevin L. McKinney

Abstract

We use the bipartite graph representation of longitudinally linked em-ployer-employee data, and the associated projections onto the employer and em-ployee nodes, respectively, to characterize the set of potential statistical summar-ies that the trusted custodian might produce. We consider noise infusion as the primary confidentiality protection method. We show that a relatively straightfor-ward extension of the dynamic noise-infusion method used in the U.S. Census Bureau’s Quarterly Workforce Indicators can be adapted to provide the same confidentiality guarantees for the graph-based statistics: all inputs have been modified by a minimum percentage deviation (i.e., no actual respondent data are used) and, as the number of entities contributing to a particular statistic increases, the accuracy of that statistic approaches the unprotected value. Our method also ensures that the protected statistics will be identical in all releases based on the same inputs.

Suggested Citation

  • John M. Abowd & Kevin L. McKinney, 2014. "Noise Infusion As A Confidentiality Protection Measure For Graph-Based Statistics," Working Papers 14-30, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:14-30
    as

    Download full text from publisher

    File URL: https://www2.census.gov/ces/wp/2014/CES-WP-14-30.pdf
    File Function: First version, 2014
    Download Restriction: no

    References listed on IDEAS

    as
    1. John M. Abowd & Bryce E. Stephens & Lars Vilhuber & Fredrik Andersson & Kevin L. McKinney & Marc Roemer & Simon Woodcock, 2009. "The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators," NBER Chapters,in: Producer Dynamics: New Evidence from Micro Data, pages 149-230 National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:14-30. See general information about how to correct material in RePEc.

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

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.