US Privacy Laws Go Against Public Preferences and Impede Public Health and Research: Survey Study (Preprint) Institutional Repository Document uri icon



    Reaping the benefits from the massive volumes of data collected in all sectors to improve population health, inform personalized medicine, and transform biomedical research requires the delicate balance between the benefits and risks of using individual-level data. There is a patchwork of US data protection laws that vary depending on the type of data, who is using it, and their intended purpose. Differences in these laws challenge big data projects using data from different sources.


    This study explores the US publics preferences for how identifiable data is used for


    We measured data use preferences of a nationally representative sample of 504 US adults using a choice-based conjoint (CBC) analysis. We selected CBC attributes and levels based on data protection laws.


    Participants strongly preferred public health and research data uses over profit-driven, marketing, or crime-detection activities. Participants also strongly preferred data uses by universities or non-profit organizations over uses by businesses and governments. Participants were fairly indifferent about different types of data used (e.g., health, education, economic).


    Our results show a notable incongruence between public preferences and current US data protection laws. Our findings appear to show that the US public favors data uses promoting social benefits over those promoting individual or organizational interests. This study provides strong support for continued efforts to provide safe access to useful datasets for research and public health. Policy-makers should consider more robust public health and research data use exceptions to align laws with public preferences.

author list (cited authors)

  • Schmit, C., Giannouchos, T., Ramezani, M., Zheng, Q. i., Morrisey, M. A., & Kum, H.

citation count

  • 0

complete list of authors

  • Schmit, Cason||Giannouchos, Theodoros||Ramezani, Mahin||Zheng, Qi||Morrisey, Michael A||Kum, Hye-Chung

Book Title

  • JMIR Preprints

publication date

  • October 2020