# Transparent Privacy is Principled Privacy

arXiv: https://arxiv.org/abs/2006.08522 · HTML: https://mlsanity.com/paper/2006.08522/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Two Views of Constrained Differential Privacy: Belief Revision and Update](https://mlsanity.com/paper/2303.00228/) (2023)
- [Differentially Private Range Queries with Correlated Input Perturbation](https://mlsanity.com/paper/2402.07066/) (2024)

## Abstract (arXiv)

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.