Steps to Tiered Access: A review of two synthetic data generation projects

Thomas M. Menino Convention & Exhibition Center 

Synthetic data generation can be used to create datasets that do not contain the exact records of the original dataset but instead retain the statistical properties of the original data. The anonymity of the original data is not compromised since synthetic data do not directly correspond with the original values. Creating synthetic data allows for a tiered access model to maximize access to data while ensuring privacy protection. However, producing synthetic data can prove challenging and resource intensive. The federal statistical community has been exploring and, at times, releasing synthetic or partially synthetic datasets for many years. With emerging needs for more access to data, particularly to help train AI/machine learning models, generating synthetic data and generating it in a way that balances privacy and utility is now at the forefront of many conversations. This talk will focus on two synthetic data projects supported through the National Secure Data Service Demonstration Project. The first explores generating synthetic data for a census survey, the Survey of Earned Doctorates, and the second explores generating synthetic data for the National Clinical Cohort Collaborative data (a large real-world high dimensional dataset with over 30 billion rows of data) in a secure super compute environment. The two talks will highlight the use of open-source code for synthetic data generation, the approach being used to assess fidelity to the original data, the opportunities and challenges with synthesizing each source, and lessons learned that can be used to inform future synthetic data generation projects. The talk will conclude with a discussion of future directions on the use of synthetic data for AI/machine learning research and other emerging needs.

National Secure Data Service

Confidentiality

Data Access