# U.S. Broadband Coverage Data Set: A Differentially Private Data Release

arXiv: https://arxiv.org/abs/2103.14035 · HTML: https://mlsanity.com/paper/2103.14035/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2021.

## Built on by (most cited first, 5 of 5)

- [Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data](https://mlsanity.com/paper/2310.19250/) (2023)
- [An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises](https://mlsanity.com/paper/2106.10241/) (2021)
- [Samplable Anonymous Aggregation for Private Federated Data Analysis](https://mlsanity.com/paper/2307.15017/) (2023)
- [SpaCE: The Spatial Confounding Environment](https://mlsanity.com/paper/2312.00710/) (2023)
- [Differential Privacy via Distributionally Robust Optimization](https://mlsanity.com/paper/2304.12681/) (2023)

## Abstract (arXiv)

Broadband connectivity is a key metric in today's economy. In an era of rapid expansion of the digital economy, it directly impacts GDP. Furthermore, with the COVID-19 guidelines of social distancing, internet connectivity became necessary to everyday activities such as work, learning, and staying in touch with family and friends. This paper introduces a publicly available U.S. Broadband Coverage data set that reports broadband coverage percentages at a zip code-level. We also explain how we used differential privacy to guarantee that the privacy of individual households is preserved. Our data set also contains error ranges estimates, providing information on the expected error introduced by differential privacy per zip code. We describe our error range calculation method and show that this additional data metric does not induce any privacy losses.