Making Differential Privacy Work for Census Data Users

• URL: http://arxiv.org/abs/2305.07208v2
• Date: Sun, 8 Oct 2023 02:36:58 GMT
• Title: Making Differential Privacy Work for Census Data Users
• Authors: Cory McCartan, Tyler Simko, and Kosuke Imai
• Abstract summary: The U.S. Census Bureau collects and publishes detailed demographic data about Americans which are heavily used by researchers and policymakers. A key output of this privacy protection system is the Noisy Measurement File (NMF), which is produced by adding random noise to tabulated statistics. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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