Balancing Data Privacy and Usability in the Federal Statistical System
Upjohn Author ORCID Identifier
PNAS 119(31): e2104906119
The federal statistical system is experiencing competing pressures for change. On the one hand, for confidentiality reasons, much socially valuable data currently held by federal agencies is either not made available to researchers at all or only made available under onerous conditions. On the other hand, agencies which release public databases face new challenges in protecting the privacy of the subjects in those databases, which leads them to consider releasing fewer data or masking the data in ways that will reduce their accuracy. In this essay, we argue that the discussion has not given proper consideration to the reduced social benefits of data availability and their usability relative to the value of increased levels of privacy protection. A more balanced benefit–cost framework should be used to assess these trade-offs. We express concerns both with synthetic data methods for disclosure limitation, which will reduce the types of research that can be reliably conducted in unknown ways, and with differential privacy criteria that use what we argue is an inappropriate measure of disclosure risk. We recommend that the measure of disclosure risk used to assess all disclosure protection methods focus on what we believe is the risk that individuals should care about, that more study of the impact of differential privacy criteria and synthetic data methods on data usability for research be conducted before either is put into widespread use, and that more research be conducted on alternative methods of disclosure risk reduction that better balance benefits and costs.
National Academy of Science
Hotz, V. Joseph, Christopher R. Bollinger, Tatiana Komarova, Charles F. Manski, Denis Nekipelov, Robert A. Moffitt, Aaron Sojourner, and Bruce D. Spencer. 2022. "Balancing Data Privacy and Usability in the Federal Statistical System." PNAS 119(31): e2104906119. https://doi.org/10.1073/pnas.2104906119