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Area of Science:

  • Statistics
  • Data Privacy
  • Social Science Research

Background:

  • Federal statistical agencies face conflicting demands: increasing data accessibility for research versus protecting subject privacy.
  • Current data privacy methods may reduce data accuracy and limit research potential.
  • Existing discussions often overlook the social benefits of data availability and usability when weighing privacy concerns.

Purpose of the Study:

  • To advocate for a balanced benefit-cost framework for assessing data privacy methods.
  • To critically evaluate synthetic data and differential privacy methods.
  • To propose improvements in measuring disclosure risk and enhancing data usability for research.

Main Methods:

  • Analysis of current pressures on the federal statistical system.
  • Critique of synthetic data methods and differential privacy criteria.
  • Proposal for a revised benefit-cost framework and risk assessment.

Main Results:

  • Current privacy protection methods, including synthetic data and differential privacy, may inadequately balance privacy with data utility.
  • The measure of disclosure risk in differential privacy may not align with risks individuals are concerned about.
  • Widespread adoption of synthetic data and differential privacy may hinder valuable research due to unknown impacts on data usability.

Conclusions:

  • A more comprehensive benefit-cost analysis is needed to guide decisions on data privacy and accessibility.
  • Disclosure risk measures should focus on risks relevant to individuals.
  • Further research is essential to understand the impact of new privacy methods on data usability and to develop better alternatives.