Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government
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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.
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.
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12 of 32 references.
References · 12
- 2022A General Framework for Auditing Differentially Private Machine Learningcited by 25
- 2023What Are the Chances? Explaining the Epsilon Parameter in Differential Privacycited by 14
- 2023The Challenge of Differentially Private Screening Rulescited by 6
- 2023Diverse Community Data for Benchmarking Data Privacy Algorithmscited by 6
- 2018Issues Encountered Deploying Differential Privacycited by 5
- 2024SoK: A Review of Differentially Private Linear Models For High-Dimensional Datacited by 5
- 2023Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profilecited by 3
- 2022Evaluation of Open-source Tools for Differential Privacycited by 2
- 2022"Am I Private and If So, how Many?" -- Using Risk Communication Formats for Making Differential Privacy Understandablecited by 2
- 2024Feature Selection from Differentially Private Correlationscited by 2
- 2023Evaluating the Usability of Differential Privacy Tools with Data Practitionerscited by 1
- 2024Centering Policy and Practice: Research Gaps around Usable Differential Privacycited by 1
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