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Balancing privacy and utility in synthetic data: Insights from Maryland’s State Longitudinal Data System

Author

Listed:
  • Woolley, Michael E.

    (Baltimore, USA)

  • Lachowicz, Mark

    (American Institutes for Research, USA)

  • Shaw, Terry V.

    (University of Maryland Baltimore, USA)

  • Bonnéry, Daniel B.

    (French Mapping Agency (Institut G.ographique National), France)

  • Henneberger, Angela K.

    (University of Maryland Baltimore, USA)

  • Feng, Yi

    (University of California Los Angeles, USA)

  • Johnson, Tessa L.

    (University of Maryland, USA)

  • Rose, Bess

    (University of Maryland Baltimore, USA)

  • Stapleton, Laura M.

    (University of Maryland, USA)

Abstract

Synthetic data hold strong potential to increase access to administrative data systems while protecting privacy for individuals. This paper details the approach taken to evaluate the synthesis of Maryland’s State Longitudinal Data System (SLDS) using fully synthetic Classification and Regression Tree (CART) models. Results demonstrate low disclosure risk (near zero) and high research utility, validated through robust evaluations. Practical insights and best practices from this case provide valuable lessons for other organisations seeking balanced synthetic data solutions for administrative data. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

Suggested Citation

  • Woolley, Michael E. & Lachowicz, Mark & Shaw, Terry V. & Bonnéry, Daniel B. & Henneberger, Angela K. & Feng, Yi & Johnson, Tessa L. & Rose, Bess & Stapleton, Laura M., 2026. "Balancing privacy and utility in synthetic data: Insights from Maryland’s State Longitudinal Data System," Journal of Data Protection & Privacy, Henry Stewart Publications, vol. 9(1), pages 38-55, August.
    • Handle: RePEc:aza:jdpp00:y:2026:v:9:i:1:p:38-55
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    JEL classification:

    • K2 - Law and Economics - - Regulation and Business Law

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