# Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government

arXiv: https://arxiv.org/abs/2410.16423 · HTML: https://mlsanity.com/paper/2410.16423/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 12 of 12)

- [A General Framework for Auditing Differentially Private Machine Learning](https://mlsanity.com/paper/2210.08643/) (2022)
- [What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy](https://mlsanity.com/paper/2303.00738/) (2023)
- [The Challenge of Differentially Private Screening Rules](https://mlsanity.com/paper/2303.10303/) (2023)
- [Diverse Community Data for Benchmarking Data Privacy Algorithms](https://mlsanity.com/paper/2306.13216/) (2023)
- [Issues Encountered Deploying Differential Privacy](https://mlsanity.com/paper/1809.02201/) (2018)
- [SoK: A Review of Differentially Private Linear Models For High-Dimensional Data](https://mlsanity.com/paper/2404.01141/) (2024)
- [Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile](https://mlsanity.com/paper/2306.15790/) (2023)
- [Evaluation of Open-source Tools for Differential Privacy](https://mlsanity.com/paper/2202.09587/) (2022)
- ["Am I Private and If So, how Many?" -- Using Risk Communication Formats for Making Differential Privacy Understandable](https://mlsanity.com/paper/2204.04061/) (2022)
- [Feature Selection from Differentially Private Correlations](https://mlsanity.com/paper/2408.10862/) (2024)
- [Evaluating the Usability of Differential Privacy Tools with Data Practitioners](https://mlsanity.com/paper/2309.13506/) (2023)
- [Centering Policy and Practice: Research Gaps around Usable Differential Privacy](https://mlsanity.com/paper/2406.12103/) (2024)

## Abstract (arXiv)

In this article, we seek to elucidate challenges and opportunities for differential privacy within the federal government setting, as seen by a team of differential privacy researchers, privacy lawyers, and data scientists working closely with the U.S. government. After introducing differential privacy, we highlight three significant challenges which currently restrict the use of differential privacy in the U.S. government. We then provide two examples where differential privacy can enhance the capabilities of government agencies. The first example highlights how the quantitative nature of differential privacy allows policy security officers to release multiple versions of analyses with different levels of privacy. The second example, which we believe is a novel realization, indicates that differential privacy can be used to improve staffing efficiency in classified applications. We hope that this article can serve as a nontechnical resource which can help frame future action from the differential privacy community, privacy regulators, security officers, and lawmakers.