Differential Privacy for Government Agencies -- Are We There Yet?
Not on the map. Why?
Abstract (arXiv)
Government agencies typically need to take potential risks of disclosure into account whenever they publish statistics based on their data or give external researchers access to collected data. In this context, the promise of formal privacy guarantees offered by concepts such as differential privacy seems to be the panacea enabling the agencies to quantify and control the privacy loss incurred by any data release exactly. Nevertheless, despite the excitement in academia and industry, most agencies -- with the prominent exception of the U.S. Census Bureau -- have been reluctant to even consider the concept for their data release strategy. This paper discusses potential reasons for this. We argue that the requirements for implementing differential privacy approaches at government agencies are often fundamentally different from the requirements in industry. This raises many challenges and questions that still need to be addressed before the concept can be used as an overarching principle when sharing data with the public. The paper does not offer any solutions to these challenges. Instead, we hope to stimulate some collaborative research efforts, as we believe that many of the problems can only be addressed by interdisciplinary collaborations.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2021.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 1; 2025: 1; 2026: 2.
8 papers.
- 2021Non-parametric Differentially Private Confidence Intervals for the Mediancited by 7
- 2022Privacy-Preserving Deep Learning Model for Covid-19 Disease Detectioncited by 3
- 2023Differentially private sliced inverse regression in the federated paradigmcited by 1
- 2023Simulation-based Bayesian Inference from Privacy Protected Datacited by 1
- 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
- 2024Evaluating utility in synthetic banking microdata applicationscited by 0
- 2025Rao Differential Privacycited by 0
- 2026It does what it says on the tin: safe synthetic data from coarsened marginscited by 0
- 2026It does what it says on the tin: safe synthetic data from coarsened margins1 Jun 2026
- 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective30 Jan 2026
- 2025Rao Differential Privacy23 Aug 2025
- 2024Evaluating utility in synthetic banking microdata applications29 Oct 2024
- 2023Simulation-based Bayesian Inference from Privacy Protected Data19 Oct 2023
- 2023Differentially private sliced inverse regression in the federated paradigm10 Jun 2023
- 2022Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection7 Sep 2022
- 2021Non-parametric Differentially Private Confidence Intervals for the Median18 Jun 2021