  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.
