Transparent Privacy is Principled Privacy

Abstract

In a technical treatment, this article establishes the necessity of transparent privacy for drawing unbiased statistical inference for a wide range of scientific questions. Transparency is a distinct feature enjoyed by differential privacy: the probabilistic mechanism with which the data are privatized can be made public without sabotaging the privacy guarantee. Uncertainty due to transparent privacy may be conceived as a dynamic and controllable component from the total survey error perspective. As the 2020 U.S. Decennial Census adopts differential privacy, constraints imposed on the privatized data products through optimization constitute a threat to transparency and result in limited statistical usability. Transparent privacy presents a viable path toward principled inference from privatized data releases, and shows great promise toward improved reproducibility, accountability, and public trust in modern data curation.

References in corpus (4)

• The Impact of the U.S. Census Disclosure Avoidance System on Redistricting and Voting Rights Analysis
• Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau's Use of Differential Privacy
• Differential Privacy of Hierarchical Census Data: An Optimization Approach
• Bias and Variance of Post-processing in Differential Privacy

Cited by in corpus (3)

• Reproducibility and Transparency versus Privacy and Confidentiality: Reflections from a Data Editor
• Congenial Differential Privacy under Mandated Disclosure
• Why Data Anonymization Has Not Taken Off