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International Conference on Privacy in Statistical Databases

PSD 2020: Privacy in Statistical Databases pp 257–270Cite as

A Synthetic Supplemental Public Use File of Low-Income Information Return Data: Methodology, Utility, and Privacy Implications

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12276)

Abstract

US government agencies possess data that could be invaluable for evaluating public policy, but often may not be released publicly due to disclosure concerns. For instance, the Statistics of Income division (SOI) of the Internal Revenue Service releases an annual public use file of individual income tax returns that is invaluable to tax analysts in government agencies, nonprofit research organizations, and the private sector. However, SOI has taken increasingly aggressive measures to protect the data in the face of growing disclosure risks, such as a data intruder matching the anonymized public data with other public information available in nontax databases. In this paper, we describe our approach to generating a fully synthetic representation of the income tax data by using sequential Classification and Regression Trees and kernel density smoothing. This synthetic data file represents previously unreleased information useful for tax policy modeling. We also tested and evaluated the tradeoffs between data utility and disclosure risks of different parameterizations using a variety of validation metrics. The resulting synthetic data set has high utility, particularly for summary statistics and microsimulation, and low disclosure risk.

Keywords

  • Disclosure control
  • Synthetic data
  • Utility
  • Classification and Regression Trees

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Acknowledgments

We are grateful to Joe Ansaldi, Don Boyd, Jim Cilke, John Czajka, Rick Evans, Dan Feenberg, Barry Johnson, Julia Lane, Graham MacDonald, Shannon Mok, Jim Nunns, James Pearce, Kevin Pierce, Alan Plumley, Daniel Silva-Inclan, Michael Strudler, Lars Vilhuber, Mike Weber, and Doug Wissoker for helpful comments and discussions.

This research is supported by a grant from Arnold Ventures. The findings and conclusions are those of the authors and do not necessarily reflect positions or policies of the Tax Policy Center or its funders.

Author information

Authors and Affiliations

  1. Urban Institute, Washington D.C., 20024, USA

    Claire McKay Bowen, Leonard Burman, Surachai Khitatrakun, Robert McClelland, Kyle Ueyama & Aaron R. Williams

  2. Internal Revenue Services, Washington D.C., 20002, USA

    Victoria Bryant

  3. Syracuse University, Syracuse, NY, 13244, USA

    Leonard Burman

  4. University of Michigan, Ann Arbor, MI, 48109, USA

    Philip Stallworth

Authors
  1. Claire McKay Bowen
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  2. Victoria Bryant
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  3. Leonard Burman
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  4. Surachai Khitatrakun
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  5. Robert McClelland
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  6. Philip Stallworth
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  7. Kyle Ueyama
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  8. Aaron R. Williams
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Corresponding author

Correspondence to Claire McKay Bowen .

Editor information

Editors and Affiliations

  1. Rovira i Virgili University, Tarragona, Catalonia, Spain

    Josep Domingo-Ferrer

  2. University of Oklahoma, Norman, OK, USA

    Krishnamurty Muralidhar

Appendix

Appendix

The appendix contains the supplementary materials to accompany the paper “A Synthetic Supplemental Public Use File of Low-Income Information Return Data: Methodology, Utility, and Privacy Implications” with additional results from the utility evaluation.

Figures 2 and 3 show the means and standard deviations, respectively, from the original and synthetic supplemental PUF data for age and all 17 tax variables. Figure 4 displays the correlation of the synthetic data minus the correlation of original data for all 17 tax variables.

Fig. 2.
figure 2

Means from the original and synthetic Supplemental PUF data. Age is on the x-axis scale, but not in dollar amounts.

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Fig. 3.
figure 3

Standard Deviations from the original and synthetic Supplemental PUF data. Age is on the x-axis scale, but not in dollar amounts.

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Fig. 4.
figure 4

Difference in Correlation (correlation of the synthetic minus the correlation of the original Supplemental PUF data).

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Bowen, C.M. et al. (2020). A Synthetic Supplemental Public Use File of Low-Income Information Return Data: Methodology, Utility, and Privacy Implications. In: Domingo-Ferrer, J., Muralidhar, K. (eds) Privacy in Statistical Databases. PSD 2020. Lecture Notes in Computer Science(), vol 12276. Springer, Cham. https://doi.org/10.1007/978-3-030-57521-2_18

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  • DOI: https://doi.org/10.1007/978-3-030-57521-2_18

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