The Nebraska Statewide Workforce & Educational Reporting System (NSWERS) is a state longitudinal data system (SLDS) that connects education and workforce data for research and policymaking. However, accessing SLDS data like those housed by NSWERS can be challenging due to privacy concerns. This nontechnical brief, intended for state longitudinal data systems stakeholders, introduces synthetic data, a privacy-enhancing technology that allows NSWERS to expand access to their research data without providing direct access to their confidential data. 

Why This Matters

SLDSs like NSWERS pose numerous privacy challenges and data-sharing opportunities. Processes that combine data across different sources and time points worsens possible privacy risks, and different data sources are subject to different federal and local privacy laws. Our work shows how SLDSs can partner with technical assistance providers like the Urban Institute to identify, develop, and deploy synthetic data solutions to meet new data user needs.

Key Takeaways

• Synthetic data methods enable confidential data holders, like NSWERS, to share data fit for different statistical purposes while limiting privacy risks associated with individuals in the NSWERS data ecosystem.
• Synthetic data, when carefully deployed, can provide greater privacy protections than traditional de-identification-based methods, like those mandated by the Family Educational Rights and Privacy Act.
• Synthetic data approaches align with the needs of many current and prospective NSWERS data users, including but not limited to data donors, subcontractors, and education policy researchers.

How We Did It

Urban Institute provided technical assistance, training, and opensource synthetic–data generation and evaluation software to NSWERS. The brief summarizes our engagement for SLDS stakeholders looking to explore or implement PETs under similar models.