Transparent Privacy is Principled Privacy

TL;DR

This paper argues that transparent privacy, exemplified by differential privacy, is essential for unbiased statistical inference, enhancing reproducibility and trust in data sharing, especially in large-scale surveys like the U.S. Census.

Contribution

It establishes transparent privacy as a necessary feature for principled inference and discusses its advantages over traditional privacy methods in statistical analysis.

Findings

Differential privacy enables public verification without compromising privacy.

Transparency in privacy mechanisms improves statistical usability.

Applying transparent privacy can enhance reproducibility and trust in data releases.

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.