DOI: 10.69554/avhs7969 ISSN: 2398-1687

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

Michael E. Woolley, Mark Lachowicz, Terry V. Shaw, Daniel B. Bonnéry, Angela K. Henneberger, Yi Feng, Tessa L. Johnson, Bess Rose, Laura M. Stapleton
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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/.

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