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Managing re-identification risks while providing access to the All of Us research program.

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The All of Us Research Program protects participant privacy by transforming data to minimize re-identification risk. While overall risk is low, certain demographics show higher potential risk.

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Area of Science:

  • Biomedical Informatics
  • Data Privacy
  • Health Research

Background:

  • The All of Us Research Program provides individual-level data access to researchers.
  • Protecting participant privacy is paramount during data dissemination.
  • Data transformation methods are crucial for mitigating re-identification risks.

Purpose of the Study:

  • To describe the privacy protections in the All of Us data access process.
  • To detail data transformation techniques used to meet re-identification risk thresholds.
  • To evaluate the re-identification risk associated with the All of Us dataset.

Main Methods:

  • Applied systematic data amendments (generalization, suppression, randomization) to mitigate re-identification.
  • Utilized a state-of-the-art adversarial model to compute participant-level re-identification risk.
  • Analyzed demographic variations in re-identification risk.

Main Results:

  • The 95th percentile of re-identification risk for all participants is below established thresholds.
  • Computed re-identification risk was confirmed to be no greater than 0.09.
  • Observed elevated re-identification risk levels for specific race, ethnic, and gender groups.

Conclusions:

  • The All of Us data access strategy effectively minimizes re-identification risk.
  • Ongoing monitoring and robust security measures supplement data protection.
  • Further investigation into demographic-specific risks is warranted.