Making Differential Privacy Work for Census Data Users

Abstract

The U.S. Census Bureau collects and publishes detailed demographic data about Americans which are heavily used by researchers and policymakers. The Bureau has recently adopted the framework of differential privacy in an effort to improve confidentiality of individual census responses. A key output of this privacy protection system is the Noisy Measurement File (NMF), which is produced by adding random noise to tabulated statistics. The NMF is critical to understanding any errors introduced in the data, and performing valid statistical inference on published census data. Unfortunately, the current release format of the NMF is difficult to access and work with. We describe the process we use to transform the NMF into a usable format, and provide recommendations to the Bureau for how to release future versions of the NMF. These changes are essential for ensuring transparency of privacy measures and reproducibility of scientific research built on census data.

arXiv comment9 pages, 2 figures

References in corpus (5)

• Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds
• Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census
• Comment: The Essential Role of Policy Evaluation for the 2020 Census Disclosure Avoidance System
• The 2020 Census Disclosure Avoidance System TopDown Algorithm
• Geographic Spines in the 2020 Census Disclosure Avoidance System

Cited by in corpus (1)

• Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods